Rural and remote early psychosis intervention services: the <scp>G</scp>ordian knot of early intervention
Bibliographic record
Abstract
AIM: One of the basic challenges of Early Psychosis Intervention (EPI) programs for rural populations is translating best practice which developed for urban high-population density areas to rural and remote settings. This paper presents data from two different models (hub and spoke and specialist outreach) of rural EPI practice in Ontario, Canada. METHODS: This cross-sectional study used a convenience sample of clients from two rural EPI programs between 2005 and 2007. Data about client outcomes specific to general functioning, admissions to hospital and emergency room (ER) visits were collected. For all dichotomous variables, chi-square tests were used to test differences between two groups. RESULTS: The total clients served in hub and spoke were 457 compared to 91 in specialist outreach. Although not statistically significant, the hub and spoke group showed better functioning in the community. There was a significant difference between the two groups with regard to hospital admissions. Although not significant, there was a greater percentage (58.3%) of specialist outreach clients who visited the ER in the previous 12 months as compared to clients serviced by the hub and spoke model (34.9%). CONCLUSIONS: The observed data from these two rural models suggest that there may be differing outcomes. There are limitations to this study, and this paper does not address why there are differences. Future work needs to continue to further explore why differences exist and whether they persist so we can provide equity and quality care for rural and remote populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".