“One program that could improve health in this neighbourhood is ____?” using concept mapping to engage communities as part of a health and human services needs assessment
Bibliographic record
Abstract
BACKGROUND: This paper presents the findings of a rapid needs assessment conducted at the request of the local health authority responsible for health care services, the Toronto Central Local Health Integration Network (Ontario, Canada), to inform health and social service planning. METHODS: We utilized concept mapping methodology to facilitate engagement with diverse stakeholders-more than 300 community members and service providers-with a focus on hard to reach populations. Key informant interviews with service providers were used to augment findings. RESULTS: Participants identified 48 unique services or service approaches they believed would improve the health of residents in the area, including those addressing health care, mental health and addictions, youth, families, people experiencing homelessness, seniors, general social services, and services targeting specific populations. While service providers consistently identified a critical need for mental health and addiction services, community members placed greater importance on the social determinants of health including access to housing, job placement supports and training and service accessibility. Both groups agreed that services and programs for seniors and people experiencing homelessness would be highly important. CONCLUSION: Our study provides a unique example of using concept mapping as a tool to aid a rapid service gap analysis and community engagement in a metropolitan area. The findings also reinforce the importance of working cross-sectorally, using a Health in All Policies approach when planning services for underserved 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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".