Incorporating Group Medical Visits into Primary Healthcare: Are There Benefits?
Bibliographic record
Abstract
OBJECTIVE: Group medical visits (GMVs) have been touted as an innovation to effectively and efficiently provide primary healthcare (PHC) services. The purpose of this paper is to report whether GMVs have tangible benefits for providers and patients. METHODS: This descriptive study included in-depth interviews with patients attending and providers facilitating GMVs and direct observation. Five primary care practices in rural towns and four First Nations communities participated. This paper reports on an analysis of interviews and observations. RESULTS: Thirty-four providers and 29 patients were interviewed. Patient participants were an average of 62 years old, mostly female and married. The three most common chronic conditions reported by patients were diabetes (n = 9), high blood pressure (n = 8) and arthritis (n = 7). Three themes illustrated how GMVs: (1) can foster access to needed health services; (2) expand opportunities for collaboration and team-based care; and (3) improve patient and provider experiences. A fourth theme captured structural challenges in delivering GMVs. DISCUSSION: There are tangible benefits in delivering GMVs in PHC. While whole patient panels can benefit from the integration of GMVs into practice, those who could gain the most are patients with complex medical and social needs. GMVs provide an opportunity to enhance PHC, strengthening the system particularly for patients with chronic conditions.
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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 | 0.019 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".