Physicians’ Tacit and Stated Policies for Determining Patient Benefit and Referral to Cardiac Rehabilitation
Bibliographic record
Abstract
BACKGROUND / PURPOSE: The benefits of prescribing cardiac rehabilitation (CR) for patients following heart surgery is well documented; however, physicians continue to underuse CR programs, and disparities in the referral of women are common. Previous research into the causes of these problems has relied on self-report methods, which presume that physicians have insight into their referral behavior and can describe it accurately. In contrast, the research presented here used clinical judgment analysis (CJA) to discover the tacit judgment and referral policies of individual physicians. The specific aims were to determine 1) what these policies were, 2) the degree of self-insight that individual physicians had into their own policies, 3) the amount of agreement among physicians, and 4) the extent to which judgments were related to attitudes toward CR. METHODS: Thirty-six Canadian physicians made judgments and decisions regarding 32 hypothetical cardiac patients, each described on 5 characteristics (gender, age, type of cardiovascular procedure, presence/absence of musculoskeletal pain, and degree of motivation) and then completed the 19 items of the Attitude towards Cardiac Rehabilitation Referral scale. RESULTS: Consistent with previous studies, there was wide variation among physicians in their tacit and stated judgment policies, and self-insight was modest. On the whole, physicians showed evidence of systematic gender bias as they judged women as less likely than men to benefit from CR. Insight data suggest that 1 in 3 physicians were unaware of their own bias. There was greater agreement among physicians in how they described their judgments (stated policies) than in how they actually made them (tacit policies). Correlations between attitude statements and CJA measures were modest. CONCLUSIONS: These findings offer some explanation for the slow progress of efforts to improve CR referrals and for gender disparities in referral rates.
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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 | 0.040 | 0.185 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".