Informing ASPIRE and a future student-run clinic: healthcare needs assessment of London, Ontario
Bibliographic record
Abstract
A needs assessment was conducted by the Alliance of Students Providing Interprofessional Resources and Education (ASPIRE) to identify gaps in healthcare delivery and health promotion within London, Ontario that can be addressed by a student-run clinic. Investigations into the social determinants of health revealed a low employment rate with notable housing and food insecurities. From a physical health perspective, cardiovascular and respiratory diseases were identified as the leading causes of hospitalization and mortality. Additionally, a high burden of mental illness and rising incidence of human immunodeficiency virus (HIV) were identified as major health challenges. Upon examination of primary care resources, various patient and practitioner-reported barriers to care were noted. Patients cited long wait times, inaccessible office hours, and inconvenient locations of practice as barriers. Primary care practitioners reported lack of resources to adequately meet the needs of complex populations, particularly those of low socioeconomic status backgrounds. To conclude, a discussion of the role and operation of a student-run clinic in addressing these findings is presented; an additional interdisciplinary healthcare resource that offers extended hours of operation, clinical outreach, and healthcare-related workshops can improve the accessibility to timely healthcare for underserved populations in London.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 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".