How do accessible veterinary care providers evaluate programmes and engage communities? Results of a qualitative analysis of Canadian service providers
Bibliographic record
Abstract
BACKGROUND: A growing number of organisations are working to address barriers to accessing veterinary care. There is limited knowledge about how such programmes develop, evolve over time to meet community needs, and how clients and communities are engaged in programme design or evaluation of impacts. Without community-engaged evaluation, programmes cannot determine the effectiveness, potential harms or broader impacts of their services. METHODS: Three focus groups and four interviews were conducted with a total of 18 accessible veterinary care providers in Canada. Transcripts were qualitatively analysed using a priori and emergent double content coding in NVivo 14. RESULTS: Thirty-three subcodes were identified across five code categories: (1) programme initiation, (2) programme evolution, (3) evaluating success, (4) ideal programme evaluation, and (5) community engagement. Participant's organisations showed large diversity in programme initiation, evolution, evaluation and community participation, reflecting the complexity of access to care and presenting an opportunity for inter-organisational knowledge sharing. Concerns about ethical community engagement, funder reporting requirements, and limited knowledge and resources hinder animal healthcare organisations' ability to effectively engage in community-based evaluation. LIMITATIONS: Potential limitations of this study include small sample size, self-selection bias, limited geographical representation, and power dynamics which can influence responses within interviews and focus groups. CONCLUSION: This study contributes to the limited literature on the development and evaluation of accessible animal healthcare care programmes in a Canadian context, and from service providers' perspectives.
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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 | 0.026 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".