Self-reported measurement systems to guide decision-making : a scoping review
Bibliographic record
Abstract
There is growing interest on founding healthcare system based on value. Value is here the outcomes acheived by patient per the cost. For a long time, high income countries took in account only the clinical outcomes measures (clinical indicator) as the important outcomes to consider. But, theses measures were limitated because they weren't captured how patients felt the cares they received especially their satisfaction of theses cares. Theses aspects are very important to assess the quality of the care patients received from the healthcare system. They are also important component of health care system performance. Thus recently, high income countries moved their interests on the use of patient reported outcomes measures (PROMS) and patient reported experiences measures (PREMS). Although , many country or theirs health regions or health facilities implementated a system to collect theses measures for clinical decision making or research ; there is no evidence to date ; that help or enhance policy decision making or decision making on goverment and health administator level. This systematic scoping review aimed to summary the implementation of self-reported measurement systems used to guide decision making at goverment level or region health system level. Methods Eligibility criteria : Intervention(s)/Exposure(s) : Measurement system aimed at patient or population health containing self-reported data Comparator(s)/Control(s): None Participants/Population : Government agencies, organizations, and administrations Outcomes : Impacts on decision making support ; Barriers and facilitators to implementation, Care quality improvement, patient-outcomes improvement Types of study to be included initially: Any type of empirical study or conference abstract Datasources : MEDLINE, Embase, CINAHL, PsychINFO, Web of Science and Academic Search Premier Data extraction : Data extraction will be conducted with a standardized, pilot-tested form by one reviewer, and another will verify. Discrepancies will be resolved by discussion or by a third reviewer (senior). Extracted data will include: characteristics of the study (ex: year of publication, review type, inclusion criteria), population (ex: level of governance, country, type of institution), interventions (ex: type of system, data collection, measurement included), and outcomes (impact, best practices, gaps). Risk of bias assessement : MMAT Data synthesis :We will present descriptive statistics (ex: means, range) to describe characteristics of included reviews. For qualitative data, we will use a content analysis approach by grouping data into themes. Data will be summarized in a narrative way. Data synthesis will focus on providing information to our knowledge users regarding the impact, best practices, gaps, and challenges. We will contextualize this information for the Canadian context. Analysis of subgroups or subsets: None Results : - Description of Self-reported measurement systems framework : process , inputs and outputs - Description of Self-reported data use to decision making : differents of use like conceptual use, persuasive use, public action -Description of barriers and facilitators
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.037 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".