Community needs assessment to inform programming
Bibliographic record
Abstract
The Church of St. John the Evangelist (“St. John’s”), serving the Kirkendall and Durand neighborhoods, seeks to expand its outreach to LGBTQ2IA+ individuals of faith and newcomers to Canada. As demographics shift and community needs evolve, St. John’s aims to adapt their services to remain relevant and inclusive. The McMaster Research Shop set out to guide St. John’s in developing targeted programming and fostering a more welcoming environment for underserved populations by researching local community needs. However, this study reveals a critical lesson in community needs assessment: the challenges of engaging community members without pre-established relationships. Our original methodology included scanning for recently published information about the needs of the communities of interest within the focus area. Finding no published information, we attempted to engage key local informants, including representatives of local social services and LGTQB2IA+ and/or immigrant neighborhood groups. Despite extensive outreach efforts, we were unable to connect with these groups. This data collection limitation emerged as a key finding, highlighting the importance of pre-existing community relationships in conducting meaningful local research and the limitations of being an “outsider.” Consequently, we pivoted to reviewing two recent city-wide needs assessments and conducting one interview with a Hamilton Immigration Partnership Council staff member to ascertain the needs of the respective populations city-wide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".