Shared decision‐making behaviours in health professionals: a systematic review of studies based on the <scp>T</scp>heory of <scp>P</scp>lanned <scp>B</scp>ehaviour
Bibliographic record
Abstract
BACKGROUND: Shared decision making (SDM) requires health professionals to change their practice. Socio-cognitive theories, such as the Theory of Planned Behaviour (TPB), provide the needed theoretical underpinnings for designing behaviour change interventions. OBJECTIVE: We systematically reviewed studies that used the TPB to assess SDM behaviours in health professionals to explore how theory is being used to explain influences on SDM intentions and/or behaviours, and which construct is identified as most influential. SEARCH STRATEGY: We searched PsycINFO, MEDLINE, EMBASE, CINAHL, Index to theses, Proquest dissertations and Current Contents for all years up to April 2012. INCLUSION CRITERIA: We included all studies in French or English that used the TPB and related socio-cognitive theories to assess SDM behavioural intentions or behaviours in health professionals. We used Makoul & Clayman's integrative SDM model to identify SDM behaviours. DATA EXTRACTION AND SYNTHESIS: We extracted study characteristics, nature of the socio-cognitive theory, SDM behaviour, and theory-based determinants of the SDM behavioural intention or behaviour. We computed simple frequency counts. MAIN RESULTS: Of 12,388 titles, we assessed 136 full-text articles for eligibility. We kept 20 eligible studies, all published in English between 1996 and 2012. Studies were conducted in Canada (n = 8), the USA (n = 6), the Netherlands (n = 3), the United Kingdom (n = 2) and Australia (n = 1). The determinant most frequently and significantly associated with intention was the subjective norm (n = 15/21 analyses). DISCUSSION: There was great variance in the way socio-cognitive theories predicted SDM intention and/or behaviour, but frequency of significance indicated that subjective norm was most influential.
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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 | 0.027 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".