Going Beyond Good Intentions: Needs Assessment for Student-Led Health Outreach in Northern Ontario
Bibliographic record
Abstract
Background: The pervasiveness of healthcare needs in the context of economic scarcity demands stewardship of healthcare resources. Despite evidence demonstrating the value of student-run clinics (SRCs), those interested in establishing or preserving SRCs are increasingly requested to conduct needs assessments. To our knowledge, no literature has described a needs assessment conducted by a developing SRC, nor how the results might inform the services offered by a SRC. This paper will explore the process, findings, and implications of a needs assessment conducted by an interprofessional team of students in Thunder Bay, Ontario, Canada.Methods: A total of 19 semi-structured interviews were conducted. Informants included: sector representatives, potential consumers, and educators. With the use of a comprehensive regional database, a local healthcare center aided with participant selection. Interviews were audio-recorded, transcribed, and qualitatively analyzed for thematic content by multiple researchers. While sector representatives and potential consumers are the subject of this paper, analysis of education interviews will be the subject of another publication.Results: The main themes encompassed existing services, primary health concerns, barriers to services, and gaps in services. The most cited health problem was drug and alcohol use. Diabetes, cardiovascular disease and mental illness were also frequently noted. Lack of basic necessities were felt to contribute to poor health. Gaps in services related to system navigation and coordination, mental health and addictions services, and preventative health services, which were felt to be more prominent for women, youth, seniors, First Nations, and LGBT populations. All participants identified opportunities for a SRC. Recommended SRC focus areas included: disease prevention, system navigation, walk-in/urgent care, counseling, safe social space, and mobile services. Some participants commented on the importance of supervision and sustainability.Conclusions: Extensive community healthcare needs were revealed which could be addressed by a SRC, while continued research exploring the effectiveness of a SRC response to a priori identified needs is warranted.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".