Where do psychologists turn to inform clinical decisions? Audience segmentation to guide dissemination strategies
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ABSTRACT:
SUBMITTER: Kwon N
PROVIDER: S-EPMC10331216 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:This study examines how unique audience segments within the Canadian population think and act toward climate change, and explores whether and how the level of audience engagement moderates the effect of various messages on support for climate policy. Drawing on a random probability sample of Canadian residents (N = 1207) conducted in October 2017, we first identify and describe five distinct audiences that vary in their attitudes, perceptions and behaviours with respect to climate change: the Alarmed (25%), Concerned (45%), Disengaged (5%), Doubtful (17%) and Dismissive (8%). We then explore how each segment responds to different messages about carbon pricing in Canada. We find that messages alluding to earmarking (i.e., "Invest in solutions") or leveling the playing field for alternative energy sources (i.e., "Relative price") increase support for a higher carbon price among the population as a whole. However, these messages decreased support for carbon pricing among more engaged audiences (e.g., Alarmed) when a low carbon price was specified to the respondent. Meanwhile, the "Relative price" is the only message that increased policy support among less engaged audiences-the Concerned and the Doubtful. In addition to highlighting the importance of tailoring and targeting messages for differently engaged segments, these results suggest that communicating around the specific consequences of carbon taxes for the prices of some goods may be a fruitful way to enhance support for carbon taxes among relatively less engaged audiences.
| S-EPMC9683580 | biostudies-literature
Project description:BackgroundSo far, audience segmentation of adolescents with respect to alcohol has been carried out mainly on the basis of socio-demographic characteristics. In this study we examined whether it is possible to segment adolescents according to their values and attitudes towards alcohol to use as guidance for prevention programmes.MethodsA random sample of 7,000 adolescents aged 12 to 18 was drawn from the Municipal Basic Administration (MBA) of 29 Local Authorities in the province North-Brabant in the Netherlands. By means of an online questionnaire data were gathered on values and attitudes towards alcohol, alcohol consumption and socio-demographic characteristics.ResultsWe were able to distinguish a total of five segments on the basis of five attitude factors. Moreover, the five segments also differed in drinking behavior independently of socio-demographic variables.ConclusionsOur investigation was a first step in the search for possibilities of segmenting by factors other than socio-demographic characteristics. Further research is necessary in order to understand these results for alcohol prevention policy in concrete terms.
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Project description:Rationale & objectiveThere is limited evidence to guide follow-up after acute kidney injury (AKI). Knowledge gaps include which patients to prioritize, at what time point, and for mitigation of which outcomes. In this study, we sought to compare the net benefit of risk model-based clinical decisions following AKI.Study designExternal validation of 2 risk models of AKI outcomes: the Grampian -Aberdeen (United Kingdom) AKI readmissions model and the Alberta (Canada) kidney disease risk model of chronic kidney disease (CKD) glomerular (G) filtration rate categories 4 and 5 (CKD G4 and G5). Process mining to delineate existing care pathways.Setting & participantsValidation was based on data from adult hospital survivors of AKI from Grampian, 2011-2013.PredictorsKDIGO-based measures of AKI severity and comorbidities specified in the original models.OutcomesDeath or readmission within 90 days for all hospital survivors. Progression to new CKD G4-G5 for patients surviving at least 90 days after AKI.Analytical approachDecision curve analysis to assess the "net benefit" of use of risk models to guide clinical care compared to alternative approaches (eg, prioritizing all AKI, severe AKI, or only those without kidney recovery).Results26,575 of 105,461 hospital survivors in Grampian (mean age, 60.9 ± 19.8 [SD] years) were included for validation of the death or readmission model, and 9,382 patients (mean age, 60.9 ± 19.8 years) for the CKD G4-G5 model. Both models discriminated well (area under the curve [AUC], 0.77 and 0.86, respectively). Decision curve analysis showed greater net benefit for follow up of all AKI than only severe AKI in most cases. Both original and refitted models provided net benefit superior to any other decision strategy. In process mining of all hospital discharges, 41% of readmissions and deaths occurred among people recovering after AKI. 1,464 of 3,776 people (39%) readmitted after AKI had received no intervening monitoring.LimitationsBoth original models overstated risks, indicating a need for regular updating.ConclusionsFollow up after AKI has potential net benefit for preempting readmissions, death, and subsequent CKD progression. Decisions could be improved by using risk models and by focusing on AKI across a full spectrum of severity. The current lack of monitoring among many with poor outcomes indicates possible opportunities for implementation of decision support.
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Project description:Optimal dose selection in clinical trials is problematic when efficacious and toxic concentrations are close. A novel quantitative approach follows for optimizing dose titration in clinical trials. A system of pharmacokinetics (PK), pharmacodynamics, efficacy, and toxicity was simulated for scenarios characterized by varying degrees of different types of variability. Receiver operating characteristic (ROC) and clinical trial simulation (CTS) were used to optimize drug titration by maximizing efficacy/safety. The scenarios included were a low-variability base scenario, and high residual (20%), interoccasion (20%), interindividual (40%), and residual plus interindividual variability scenarios, and finally a shallow toxicity slope scenario. The percentage of subjects having toxicity was reduced by 87.4% to 93.5%, and those having efficacy was increased by 52.7% to 243%. Interindividual PK variability may have less impact on optimal cutoff values than other sources of variability. ROC/CTS methods for optimizing dose titration offer an individualized approach that leverages exposure-response relationships.
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Project description:AimDevelop and apply a comprehensive and accurate next-generation sequencing based assay to help clinicians to match oncology patients to therapies.Materials & methodsThe performance of the CANCERPLEX® assay was assessed using DNA from well-characterized routine clinical formalin-fixed paraffin-embedded (FFPE) specimens and cell lines.ResultsThe maximum sensitivity of the assay is 99.5% and its accuracy is virtually 100% for detecting somatic alterations with an allele fraction of as low as 10%. Clinically actionable variants were identified in 93% of patients (930 of 1000) who underwent testing.ConclusionThe test's capacity to determine all of the critical genetic changes, tumor mutation burden, microsatellite instability status and viral associations has important ramifications on clinical decision support strategies, including identification of patients who are likely to benefit from immune checkpoint blockage therapies.
| S-EPMC5580078 | biostudies-literature
Project description:The practice of medicine relies on the patient-physician relationship, knowledge, and clinical judgment. Randomized controlled trials (RCTs) remain the least biased method for studying the effects of interventions in selected populations and are the only method to control adequately for unknown confounders. However, physicians face the limitations of RCTs on a daily basis as they treat relatively unselected populations and individual patients. We explore the benefits and limitations of RCTs for some neurologic disorders, and discuss the difficulties of predicting individualized outcomes and anticipating treatment responses in those heterogeneous conditions. Observational studies and advances in understanding neurologic diseases complement RCTs in decision-making. Considerable challenges remain for personalized medicine; for now, clinicians must rely on their ability to integrate evidence and clinical judgment.
| S-EPMC5764529 | biostudies-other
Project description: Not available
| S-EPMC7617474 | biostudies-literature
Project description:Study designA longitudinal retrospective study.ObjectiveTo better understand individual-level temporal change in functional status for participants with paraplegia in the National Spinal Cord Injury Database (NSCID), as measured by Rasch Transformed Motor Functional Indepedence Measure (FIM) scores.SettingMulticenter/Multistate longitudinal study across the United States.MethodsNon-linear random effects modeling, that is, individual growth curve analysis of retrospective data obtained from the National Institute on Disability and Rehabilitation Research (NIDRR) NSCID.ResultsWe generated non-linear individual level trajectories of recovery for Rasch Transformed Motor FIM scores that rise rapidly from inpatient rehabilitation admission to a plateau. Trajectories are based on relationships between growth parameters and patient and injury factors: race, gender, level of education at admission, age at injury, neurological level at discharge, American Spinal Injury Association Impairment Scale (AIS) at discharge, days from injury to first system inpatient rehabilitation admission, rehabilitation length of stay, marital status and etiology. On the basis of study results, an interactive tool was developed to represent individual level longitudinal outcomes as trajectories based upon an individual's given baseline characteristics, that is, information supplied by the covariates and provides a robust description of temporal change for those with paraplegia within the NSCID.ConclusionsThis methodology allows researchers and clinicians to generate and better understand patient-specific trajectories through the use of an automated interactive tool where a nearly countless number of longitudinal paths of recovery can be explored. Projected trajectories holds promise in facilitating planning for inpatient and outpatient services, which could positively impact long term outcomes.
| S-EPMC5578744 | biostudies-literature
Project description:BackgroundElected officials (e.g., legislators) are an important but understudied population in dissemination research. Audience segmentation is essential in developing dissemination strategies that are tailored for legislators with different characteristics, but sophisticated audience segmentation analyses have not been conducted with this population. An empirical clustering audience segmentation study was conducted to (1) identify behavioral health (i.e., mental health and substance abuse) audience segments among US state legislators, (2) identify legislator characteristics that are predictive of segment membership, and (3) determine whether segment membership is predictive of support for state behavioral health parity laws.MethodsLatent class analysis (LCA) was used. Data were from a multi-modal (post-mail, e-mail, telephone) survey of state legislators fielded in 2017 (N = 475). Nine variables were included in the LCA (e.g., perceptions of behavioral health treatment effectiveness, mental illness stigma). Binary logistic regression tested associations between legislator characteristics (e.g., political party, gender, ideology) and segment membership. Multi-level logistic regression assessed the predictive validity of segment membership on support for parity laws. A name was developed for each segment that captured its most salient features.ResultsThree audience segments were identified. Budget-oriented skeptics with stigma (47% of legislators) had the least faith in behavioral health treatment effectiveness, had the most mental illness stigma, and were most influenced by budget impact. This segment was predominantly male, Republican, and ideologically conservative. Action-oriented supporters (24%) were most likely to have introduced a behavioral health bill, most likely to identify behavioral health issues as policy priorities, and most influenced by research evidence. This was the most politically and ideologically diverse segment. Passive supporters (29%) had the greatest faith in treatment effectiveness and the least stigma, but were also least likely to have introduced a behavioral health bill. Segment membership was a stronger predictor of support for parity laws than almost all other legislator characteristics.ConclusionsState legislators are a heterogeneous audience when it comes to behavioral health. There is a need to develop and test behavioral health evidence dissemination strategies that are tailored for legislators in different audience segments. Empirical clustering approaches to audience segmentation are a potentially valuable tool for dissemination science.
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Project description:Memory enables access to past experiences to guide future behavior. Humans can determine which memories to trust (high confidence) and which to doubt (low confidence). How memory retrieval, memory confidence, and memory-guided decisions are related, however, is not understood. In particular, how confidence in memories is used in decision making is unknown. We developed a spatial memory task in which rats were incentivized to gamble their time: betting more following a correct choice yielded greater reward. Rat behavior reflected memory confidence, with higher temporal bets following correct choices. We applied machine learning to identify a memory decision variable and built a generative model of memories evolving over time that accurately predicted both choices and confidence reports. Our results reveal in rats an ability thought to exist exclusively in primates and introduce a unified model of memory dynamics, retrieval, choice, and confidence.
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