Family medicine physician teachers and residents’ intentions to prescribe and interpret spirometry: a descriptive cross-sectional study
Bibliographic record
Abstract
Background: Spirometry is the best test to demonstrate airway obstruction, but remains underused in primary care. Objectives: We assessed, among family medicine physician teachers and residents, their intention to prescribe spirometry in patients suspected of chronic obstructive pulmonary disease and their intention to interpret the results. This evaluation is based on the theoretical framework proposed by Godin et al. for the study of factors influencing healthcare professionals’ behavior. Methods: Participants of this descriptive cross-sectional study were recruited from eight Family medicine units (FMUs) of Laval University’s network. They completed a 23-item self-administered questionnaire measuring their intention to prescribe and to interpret spirometry as well as some determinants of this intention (beliefs about capabilities, beliefs about consequences, social influence and moral norm). Answers to each of the items were scored on a Likert scale (score 1 to 7) where a higher score indicated a greater agreement with the statement. Results: Of the 284 eligible physicians, 104 were included. The mean score ± standard deviation of physicians' intention to prescribe spirometry (6.6 ± 0.7) was higher than to interpret the results (5.8 ± 1.5). Mean scores for all determinants of intention measured were also higher for prescription than for interpretation of spirometry. Conclusion: The results suggest that participants have a very strong intention to prescribe spirometry. Although the intention to interpret the results is positive, it is weaker than for the prescription of the test. Further studies will be needed to assess the barriers to spirometry interpretation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".