Improved preventive care in family practices with outreach facilitation: understanding success and failure
Bibliographic record
Abstract
OBJECTIVES: To understand why some family practices with a facilitator improved preventive performance more than others. Sustainability of practice improvements one year after the intervention was also explored. METHODS: Interviews with physicians and nurses from seven practices and data gathered during the intervention were used to form case studies of three high performing and four low performing family practices. Case studies were developed using cross-case analysis with a combination of the constant-comparative method and memoing-diagramming. Two researchers independently conducted in-depth coding of transcripts and documents, individual case construction for each study site, and then cross-case analysis of the identified themes between study sites. RESULTS: Staff involvement and a positive attitude toward implementation of changes were central to high improvement in performance. A lack of computers, low staff involvement or high staff turnover were associated with low improvement in performance. Personal characteristics of the facilitator are important. Six of the seven practices still had the prevention tools in place one year after the intervention and all noted that participation had improved their understanding of preventive medicine. CONCLUSIONS: When using facilitators, one should avoid practices in turmoil, strive for continuity over time, and recognise the importance of the relationship between the facilitator and the practice.
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 | 0.031 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".