Delivering Primary Care to Homeless Persons: A Policy Analysis Approach to Evaluating the Options
Bibliographic record
Abstract
Homeless persons are numerous, carry a significant burden of illness and face challenges in accessing care.A search of the literature revealed insufficient empirical sources to permit the use of standard systematic review methodology to determine the most effective way to deliver point-of-first-contact healthcare to homeless people.Instead, we used a policy analysis approach.We found that the dominant model of primary care in Canada performs poorly when assessed on 13 evaluation criteria.While there is variable performance on individual measures, the three alternative models -targeted standard facility/clinic site, fixed outreach site and mobile outreach service -all perform well.Our findings suggest that some factor other than performance on the specified measures, such as costs, feasibility, geographical fit or local preferences, should be used to choose a specific model.Our analysis clearly indicates that the status quo model of primary care is inadequate to meet the needs of homeless people. RésuméLes sans-abri sont nombreux, ils doivent surmonter de durs problèmes de santé et font face à des défis d' accessibilité quant aux soins de santé.Nos recherches dans la littérature n' ont pas permis d' amasser suffisamment de sources empiriques pour mener une revue systématique méthodologiquement acceptable afin de déterminer les façons les plus efficaces d' offrir des points d' accès de première ligne pour les sans-abri.Nous avons donc employé une méthode d' analyse des politiques.Nous avons découvert que, selon les 13 critères d' évaluation utilisés, le modèle actuel des soins de santé primaires au Canada présente un faible rendement.Bien que le rendement varie pour ce qui Delivering Primary Care to Homeless Persons: A Policy Analysis Approach to Evaluating the Options
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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 | 0.189 | 0.175 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".