Attitudes, perceptions and awareness concerning quaternary prevention among family doctors working in the Social Security System, Peru: a cross-sectional descriptive study
Bibliographic record
Abstract
BACKGROUND: Quaternary Prevention is defined as the action taken to identify patients at risk of overtreatment, to protect them from additional medical treatments, and to suggest interventions that are ethically acceptable. Many countries and organizations have joined in the efforts to practice quaternary prevention. These countries started a campaign called Choosing Wisely that implements recommendations in order to avoid harming patients. OBJECTIVE: To determine the attitudes, perceptions and awareness towards Quaternary Prevention and the practice of Choosing Wisely Canada Recommendations among family doctors working in the Social Security System in Peru. METHODS: A questionnaire was developed after reviewing the literature and contacting experts in the field and was sent by email to all 64 family physicians in the Social Security System (Essalud) in Lima Peru. Responses were received from 40 participants. RESULTS: The response rate was 64%. Approximately 95% reported that they understand the concept of quaternary prevention. Agreement with all the recommendations was 90% or higher. In most of the recommendations the applicability was more than 80%. The most important barriers perceived for the practice of Quaternary Prevention were patients expectations (33%). CONCLUSIONS: There are positive perceptions towards Quaternary Preventions and Choosing Wisely recommendations in the family doctors of social security in Lima Peru.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".