The Feasibility of a Primary Care Based Navigation Service to Support Access to Health and Social Resources: The Access to Resources in the Community (ARC) Model.
Bibliographic record
Abstract
Introduction: We established a patient centric navigation model embedded in primary care (PC) to support access to the broad range of health and social resources; the Access to Resources in the Community (ARC) model. Methods: We evaluated the feasibility of ARC using the rapid cycle evaluations of the intervention processes, patient and PC provider surveys, and navigator log data. PC providers enrolled were asked to refer patients in whom they identified a health and/or social need to the ARC navigator. Results: Participants: 26 family physicians in four practices, and 82 of the 131 patients they referred. ARC was easily integrated in PC practices and was especially valued in the non-interprofessional practices. Patient overall satisfaction was very high (89%). Sixty patients completed the post-intervention surveys, and 33 reported accessing one or more service(s). Conclusion: The ARC Model is an innovative approach to reach and support a broad range of patients access needed resources. The Model is feasible and acceptable to PC providers and patients, and has demonstrated potential for improving patients' access to health and social resources. This study has informed a pragmatic randomized controlled trial to evaluate the ARC navigation to an existing web and telephone navigation service (Ontario 211).
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".