Novel Interprofessional Mentoring Intervention to Improve Spirometry in Primary Care: Uptake, Feedback, and Effects on Behavioral Intention
Bibliographic record
Abstract
INTRODUCTION: Little is known about the nature and effects of mentoring interventions on evidence-based clinician behaviors. We sought to design and evaluate a novel mentorship-based intervention to improve the usage of spirometry in primary care. METHODS: This was a prospective one-year study of a pragmatic intervention across Canadian primary care sites. We established mentor-mentee pods, each including physician and nurse/allied health mentors and mentees, and enabled communication through a secure online portal; email; telephone; teleconference; videoconference; fax; and/or in person. We measured (1) change in intention to perform spirometry (through a questionnaire based on the theory of planned behavior, administered before and after the intervention); (2) mentoring uptake; and (3) feedback/satisfaction. RESULTS: Twenty-five of 90 (28%) nurse/allied health and 23/68 (34%) physician mentees consented across seven sites. There were no statistically significant changes in behavioral intention after the intervention. Mentors logged 56.5 hours, with most preferred communication modalities being in person (6/11; 55%) and email (4/11; 36%). Mentees most commonly used email (9/18; 50%), followed by in-person communication (6/18; 33%). Mentees were highly satisfied with the experience, and most (89%) would participate in a similar program again. DISCUSSION: A mentorship-based intervention can successfully engage physicians, nurses, and allied health practitioners through multiple communication platforms. Email seems to be an important medium for this activity. Such interventions can be highly satisfying and may affect certain constructs underlying mentees' behavioral intentions. Such a program can be replicated across diseases, and future research should measure effects on behavior, patient outcomes, and the sustainability of effects.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".