Making decisions about cancer screening when the guidelines are unclear or conflicting.
Bibliographic record
Abstract
OBJECTIVE: Our purpose was to determine the factors involved in the cancer screening decisions of family physicians in situations where the clinical practice guidelines are unclear or conflicting as opposed to when they are clear and uncontroversial. STUDY DESIGN: We analyzed discussions with focus groups using a constant comparative approach. POPULATION: A total of 73 family physicians in active practice participated in 10 focus groups (1 urban group and 1 rural group in each of 5 Canadian provinces). OUTCOME MEASURES: Our main outcome measures were participants' perceptions regarding cancer screening when the guidelines were unclear or conflicting. RESULTS: We propose a model of the determinants of cancer screening decision making with regard to unclear and conflicting guidelines. This model is rooted in the physician-patient relationship, and is an interactive process influenced by patient factors (anxiety, expectations, and family history) and physician factors (perception of guidelines, clinical practice experience, influence of colleagues, distinction between the screening styles of specialists and family physicians, and the amount of time and financial costs involved in performing the maneuver). CONCLUSIONS: Our model is unique, because it is embedded in the physician-patient relationship. Ultimately, a modified model could be used to design interventions to assist with the implementation of preventive services guidelines.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".