Reflective Tool on Advanced Access to Support Primary Healthcare Teams: Development and Validation of an Online Questionnaire
Bibliographic record
Abstract
RATIONALE: Awareness of their standing relative to best practices motivates primary healthcare (PHC) teams to improve their practices. However, gathering the data necessary to create such a portrait is a challenge. An effective way to support the improvement of the practices of PHC teams is to simplify the availability of data portraying aspects of their practices that might need improvement. Timely access is one of the foremost challenges of PHC. Yet, very few tools supporting reflections on the implementation of best practices to improve access are available to PHC teams. AIMS AND OBJECTIVES: To develop an online reflective tool that evaluates the state of a PHC team member's advanced access practice and formulates customized recommendations for improvement. METHODS: This sequential multimethod study was informed by a literature review and an expert panel composed of researchers, patients, provincial and local decision-makers, and PHC clinical and administrative staff in the province of Quebec, Canada. Consensus was reached on the content of the questionnaire and the prioritization of the recommendations. RESULTS: No reflective tool on advanced access practices was found in the literature review. Grey literature was used to create an initial version of the questionnaire. This version was revised and enriched through consultation phases with the expert panel. Then, five iterations of the tool were tested with 169 PHC team members, which led to the conception of two distinct versions: one for clinical staff and one for administrative agents responsible for appointment booking. The final versions of the reflective tool are available online in both English and French. CONCLUSION: This reflective tool provides a portrait of PHC team members' advanced access practices as well as an automated report that contains personalized and prioritized recommendations for improvement. Further developments are necessary for its optimal use among PHC professionals other than physicians and nurse practitioners.
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 | 0.087 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".