Effects of a 12-month multi-faceted mentoring intervention on knowledge, quality, and usage of spirometry in primary care: a before-and-after study
Bibliographic record
Abstract
BACKGROUND: Asthma is among the most common chronic diseases in adults. International guidelines have emphasized the importance of regular spirometry for asthma control evaluation. However, spirometry use in primary care remains low across jurisdictions. We sought to design and evaluate a knowledge translation intervention to address both the poor quality of spirometry and the underuse of spirometry in primary care. METHODS: We designed a 1-year intervention consisting of initial interactive education and hands-on training followed by unstructured peer expert mentoring (through an online portal, email, telephone, videoconference, fax, and/or in-person). We recruited physician and allied health mentees from across primary care sites in Ontario, Canada. We compared spirometry-related knowledge immediately before and after the 1-year intervention period and the quality of spirometry testing and the usage of spirometry in patients with asthma in the year before and the year of the intervention. RESULTS: Seven of 10 (70 %) invited sites participated, including 25/90 (28 %) invited allied health mentees and 23/68 (34 %) invited physician mentees. We recruited 7 physician mentors and 4 allied health mentors to form 3 mentor-mentee pods. Spirometry knowledge scores increased from 21.4 +/- 3.1 pre- to 27.3 +/- 3.5 (out of 35) (p < 0.01) post-intervention. Spirometry acceptability and repeatability criteria were met by 59/191 (30.9 %) spirometries and 86/193 (44.6 %) spirometries [odds ratio 1.7 (1.0, 3.0)], in the pre-intervention and intervention periods, respectively. Spirometry was ordered in 75/512 (14.6 %) and 129/336 (38.4 %) respiratory visits (p < 0.01), and in 20/3490 (0.6 %) and 36/2649 (1.4 %) non-respiratory visits (p < 0.01), in the pre-intervention and intervention periods, respectively. CONCLUSIONS: A mentorship-based intervention involving physicians and allied health team members can enhance knowledge, quality, and actual use of spirometry in real world primary care settings. A future controlled study should assess the impact of this intervention on patient outcomes, its cost-effectiveness, and its sustainability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".