Preventive care checklist form. Evidence-based tool to improve preventive health care during complete health assessment of adults.
Bibliographic record
Abstract
PROBLEM ADDRESSED: Preventive care is a cornerstone in the practice of family medicine, but is often difficult to provide because of a lack of time and logistic difficulties. OBJECTIVE OF PROGRAM: To develop an evidence-based practice-relevant preventive care checklist form to be used by family physicians during complete health assessment of adults. PROGRAM DESCRIPTION: Guidelines for preventive health care of adults at average risk from the Canadian Task Force on Preventive Health Care and from other Canadian sources where the Task Force guidelines were not up-to-date were reviewed. Checklist forms covering recommended preventive health care maneuvers were created. The forms incorporate evidence-based preventive care guidelines as well as non-evidence-based components that are a part of routine practice. The forms require few resources to implement, are cost-effective, and are easy to use. The forms list items needed to meet provincial billing requirements for complete health assessments and have space for physicians to make notes. The forms can be used electronically or printed off and photocopied for use in paper-based charts. CONCLUSION: The Preventive Care Checklist Form is a low-cost, easy-to-use tool that merges practice maneuvers with evidence-based recommendations. It could help improve preventive care practices in Canada.
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.009 |
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".