Questioning context: a set of interdisciplinary questions for investigating contextual factors affecting health decision making
Bibliographic record
Abstract
OBJECTIVE: To combine insights from multiple disciplines into a set of questions that can be used to investigate contextual factors affecting health decision making. BACKGROUND: Decision-making processes and outcomes may be shaped by a range of non-medical or 'contextual' factors particular to an individual including social, economic, political, geographical and institutional conditions. Research concerning contextual factors occurs across many disciplines and theoretical domains, but few conceptual tools have attempted to integrate and translate this wide-ranging research for health decision-making purposes. METHODS: To formulate this tool we employed an iterative, collaborative process of scenario development and question generation. Five hypothetical health decision-making scenarios (preventative, screening, curative, supportive and palliative) were developed and used to generate a set of exploratory questions that aim to highlight potential contextual factors across a range of health decisions. FINDINGS: We present an exploratory tool consisting of questions organized into four thematic domains - Bodies, Technologies, Place and Work (BTPW) - articulating wide-ranging contextual factors relevant to health decision making. The BTPW tool encompasses health-related scholarship and research from a range of disciplines pertinent to health decision making, and identifies concrete points of intersection between its four thematic domains. Examples of the practical application of the questions are also provided. CONCLUSIONS: These exploratory questions provide an interdisciplinary toolkit for identifying the complex contextual factors affecting decision making. The set of questions comprised by the BTPW tool may be applied wholly or partially in the context of clinical practice, policy development and health-related research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.082 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".