The challenges of implementing advanced access for residents in family medicine in Quebec. Do promising strategies exist?
Bibliographic record
Abstract
BACKGROUND: The advanced access (AA) model is a highly recommended innovation to improve timely access to primary healthcare. Despite that many studies have shown positive impacts for healthcare professionals, and for patients, implementing this model in clinics with a teaching mission for family medicine residents poses specific challenges. OBJECTIVE: To identify these challenges within these clinics, as well as potential strategies to address them. DESIGN: The authors adopted a qualitative multiple case study design, collected data in 2016 using semi-structured interviews (N = 40) with healthcare professionals and clerical staff in four family medicine units in Quebec, and performed a thematic analysis. They validated results through a discussion workshop, involving many family physicians and residents practicing in different regions Results: Five challenges emerged from the data: 1) choosing, organizing residents' patient; 2) managing and balancing residents' appointment schedules; 3) balancing timely access with relational continuity; 4) understanding the AA model; 5) establishing collaborative practices with other health professionals. Several promising strategies were suggested to address these challenges, including clearly defining residents' patient panels; adopting a team-based care approach; incorporating the model into academic curriculum and clinical training; proactive and ongoing education of health professionals, residents, and patients; involving residents in the change process and in adjustment strategies. CONCLUSIONS: To meet the challenges of implementing AA, decision-makers should consider exposing residents to AA during academic training and clinical internships, involving them in team work on arrival, engaging them as key actors in the implementation and in intra- and inter-professional collaborative models.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".