Adherence to cervical cancer screening guidelines by family physicians in Calgary: cross-sectional analysis using demographic characteristics and lab data
Bibliographic record
Abstract
Abstract Background: The effectiveness of cervical cancer screening programs is well recognized; however, inappropriate screening practices result in either a woman being tested too often or not tested at the recommended intervals. The objectives of the study were to identify the characteristics of family physicians associated with over- and underscreening for eligible women aged 25-69 in Calgary, Alberta. Methods: We performed a population-based retrospective observational study linking the Calgary Laboratory Services database from 2014-2016 to the College of Physicians and Surgeons of Alberta database of family physicians practicing in Calgary. We matched physicians’ characteristics with their cervical screening practices. Panel size data was not directly available, so we used their laboratory test orders in 2016 as a proxy measure to estimate physician practice size. For the underscreening analysis, we excluded those physicians whose estimated practice size was lower than the number of screening tests ordered. Logistic regression models were applied to analyze the overscreening and underscreening patterns. Results: Among 807 physicians included in the overscreening analysis, 43% of physicians had over-screened their screen-eligible patients. Physician characteristics significantly associated with overscreening included more years of practice and having more female patients in the practice. Among the 317 physicians included in the underscreening analysis, 42% had under-screened during the three-year study period. Female physicians were less likely to underscreen their eligible female patients. Physicians practicing in the Northeast quadrant of the city also had higher odds of underscreening. Conclusions: Screening patterns of family physicians indicate both overuse and underuse, which indicates inconsistency in adherence to screening guideline recommendations. Addressing disparities and identifying strategies to improve guideline adherence among different physician demographic groups is critical for the success of screening programs.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".