Assessments of the extent to which health‐care providers involve patients in decision making: a systematic review of studies using the <scp>OPTION</scp> instrument
Bibliographic record
Abstract
BACKGROUND: We have no clear overview of the extent to which health-care providers involve patients in the decision-making process during consultations. The Observing Patient Involvement in Decision Making instrument (OPTION) was designed to assess this. OBJECTIVE: To systematically review studies that used the OPTION instrument to observe the extent to which health-care providers involve patients in decision making across a range of clinical contexts, including different health professions and lengths of consultation. SEARCH STRATEGY: We conducted online literature searches in multiple databases (2001-12) and gathered further data through networking. INCLUSION CRITERIA: (i) OPTION scores as reported outcomes and (ii) health-care providers and patients as study participants. For analysis, we only included studies using the revised scale. DATA EXTRACTION: Extracted data included: (i) study and participant characteristics and (ii) OPTION outcomes (scores, statistical associations and reported psychometric results). We also assessed the quality of OPTION outcomes reporting. MAIN RESULTS: We found 33 eligible studies, 29 of which used the revised scale. Overall, we found low levels of patient-involving behaviours: in cases where no intervention was used to implement shared decision making (SDM), the mean OPTION score was 23 ± 14 (0-100 scale). When assessed, the variables most consistently associated with higher OPTION scores were interventions to implement SDM (n = 8/9) and duration of consultations (n = 8/15). CONCLUSIONS: Whatever the clinical context, few health-care providers consistently attempt to facilitate patient involvement, and even fewer adjust care to patient preferences. However, both SDM interventions and longer consultations could improve this.
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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 | 0.038 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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".