Academic family health teams
Bibliographic record
Abstract
Objective To explore patients’ perceptions of primary care (PC) in the early development of academic family health teams (aFHTs)—interprofessional PC teams delivering care where family medicine and other health professional learners are trained—focusing on the 4 core domains of PC. Design Self-administered survey using the Primary Care Assessment Tool Adult Expanded Version (PCAT), which addresses 4 core domains of PC (first contact, continuity, comprehensiveness, and coordination). The PCAT uses a 4-point Likert scale (from definitely not to definitely) to capture patients’ responses about the occurrence of components of care. Setting Six aFHTs in Ontario. Participants Adult patients attending appointments and administrators at each of the aFHTs. Main outcome measures Mean PCAT domain scores, with a score of 3 chosen as the minimum expected level of care. Multivariate log binomial regression models were used to estimate the adjusted relative risks of PCAT score levels as functions of patient- and clinic-level characteristics. Results The response rate was 47.3% (1026 of 2167). The mean age of respondents was 49.6 years, and most respondents were female (71.6%). The overall PC score (2.92) was just below the minimum expected care level. Scores for first contact (2.28 [accessibility]), coordination of information systems (2.67), and comprehensiveness of care (2.83 [service available] and 2.36 [service provided]) were below the minimum. Findings suggest some patient groups might not be optimally served by aFHTs, particularly recent immigrants. Characteristics of aFHTs, including a large number of physicians, were not associated with high performance on PC domains. Distributed practices across multiple sites were negatively associated with high performance for some domains. The presence of electronic medical records was not associated with improved performance on coordination of information systems. Conclusion Patients of these aFHTs rated several core domains of PC highly, but results indicate room for improvement in several domains, particularly first-contact accessibility. A future study will determine what changes were implemented in these aFHTs and if patient ratings have improved. This reflective process is essential to ensuring that aFHTs provide effective models of PC to learners of all disciplines.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.003 |
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".