Enhancing prevention in primary care: are interventions targeted towards consumers’ and providers’ perceived needs?
Bibliographic record
Abstract
OBJECTIVE: To explore perceived barriers to the implementation of prevention guidelines, with a particular interest to perceived information needs from the point of view of health professionals and consumers. STUDY DESIGN: Focus group. SETTING AND PARTICIPANTS: Eight focus groups were held in three Canadian cities: three with consumer, three with family physician, and two with primary care nurses. ANALYSIS: Inductive analysis based on transcribed interviews. The material was analysed by two of the investigators. Agreement on interpretation was checked independently by three other researchers on 10% of the material. RESULTS: Lack of motivation, discontinuity of care and lack of adequate remuneration were perceived as the strongest barriers to prevention implementation. Computerized information management systems were not perceived by physicians and nurses as strong facilitating factors. Consumers expressed strongly a need for information on non-traditional preventive interventions. Physicians and nurses expressed a need for patient education material more than for practice guidelines. Research evidence was not considered as the first criteria to judge the value of preventive information. CONCLUSIONS: Evidence-based medicine has triggered a massive effort to develop technologies to support the dissemination of evidence-based information on the assumption that poor access to such information is an important barrier to implementation of effective practices. Our results suggest that such an assumption may not be correct. Providing only evidence-based information from the realm of traditional medicine will appear restrictive to most users, particularly to consumers, and may not be as valued as anticipated considering the expressed scepticism toward research evidence.
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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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".