MétaCan
Menu
Back to cohort
Record W4317895727 · doi:10.1370/afm.21.s1.3646

Compatibility of Quebec Residency Program Characteristics with the Advanced Access Model: A Cross-Sectional Study

2023· article· en· W4317895727 on OpenAlexaboutno aff
Marie-Ève Boulais, D. Perrier, François Racine-Hemmings, Nadia Deville‐Stoetzel, Mylaine Breton, Isabelle Gaboury

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsFamily medicineMedical educationCross-sectional studyPillarMedicinePsychologyEngineering

Abstract

fetched live from OpenAlex

Context The advanced access (AA) model is based on several core pillars, including balancing appointment supply and demand, regularly adjusting supply, optimizing appointment systems and collaboration practices. Exposure of family medicine resident to AA within university family medicine groups (U-FMGs) is a promising strategy to widen its dissemination. It is also essential to reflect on aspects of the model that could be adopted as well as those requiring adaptation to improve the residency experience. Objective Determine compatibility of U-FMG residency programs with AA pillars. Study Design and Analysis Cross-sectional survey of local residency program characteristics with respect to AA pillars. Program compatibility with 4 AA pillars was categorized inductively by a research team that included clinicians, AA experts, a residency program expert and 4 family medicine residents. Setting 46 U-FMGs in Quebec, Canada Population studied The chief resident and academic director of each U-FMG Instrument A de novo online survey questionnaire piloted with 3 former chief residents. The survey included 32 questions about family group practices, residency rotation programs, opportunities to collaborate with other professionals and training received on AA. The questionnaire and 2 reminders were sent by email to all respondents. Outcome measures Characteristics of residency programs were grouped by AA pillar. A three-level score (compatible, moderately compatible, or not very compatible with the principles of AA) was assigned to each group of characteristics of local U-FMG residency programs. Results 38 of 46 U-FMGs participated (82.6%). No U-FMG obtained a score of compatible for all 4 pillars considered. Balancing appointment supply and demand appeared to be adequate for >70% of U-FMGs. However, 60% of U-FMG appointment systems were not very compatible with the AA model, mostly because the proportion of the schedule reserved for urgent appointments was insufficient. Opportunities for collaboration were compatible with AA principles in 32% of programs. Almost 66% of programs offered training on AA. Conclusions Our study highlights the heterogeneity among residency programs with respect to their compatibility with AA. Our theoretical approach limits the objective assessment of the impact on timely access for patients. A second part of the project correlating AA indicators with residency program characteristics is ongoing.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.987
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.063
GPT teacher head0.367
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicHealthcare Systems and TechnologyFrench-language works237,207