Open Access Community capacity to acquire, assess, adapt,
Bibliographic record
Abstract
Background: Community-based organizations (CBOs) are important stakeholders in health systems and are increasingly called upon to use research evidence to inform their advocacy, program planning, and service delivery. To better support CBOs to find and use research evidence, we sought to assess the capacity of CBOs in the HIV/ AIDS sector to acquire, assess, adapt, and apply research evidence in their work. Methods: We invited executive directors of HIV/AIDS CBOs in Ontario, Canada (n = 51) to complete the Canadian Health Services Research Foundation’s “Is Research Working for You? ” survey. Findings: Based on responses from 25 organizations that collectively provide services to approximately 32,000 clients per year with 290 full-time equivalent staff, we found organizational capacity to acquire, assess, adapt, and apply research evidence to be low. CBO strengths include supporting a culture that rewards flexibility and quality improvement, exchanging information within their organization, and ensuring that their decision-making processes have a place for research. However, CBO Executive Directors indicated that they lacked the skills, time, resources, incentives, and links with experts to acquire research, assess its quality and reliability, and summarize it in a userfriendly way. Conclusion: Given the limited capacity to find and use research evidence, we recommend a capacity-building
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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 | 0.089 | 0.242 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.054 | 0.009 |
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".