Realizing an Evidence-Based Framework for the Management and Delivery of Family Support Services
Bibliographic record
Abstract
PURPOSE: This paper reports on how service providers and academic researchers partnered to support the journey of a primary prevention organization in western Canada as they reviewed their programming against an evidence-based practice (EBP) framework. The process allows the organization to increase their EBP culture by encouraging staff understanding and uptake of their nine family support programs. It also informs service users and other stakeholders of the evidentiary status of different kinds of support services. In this way, the families become more informed and engaged partners who might easily evaluate the respective risks and benefits of the various applications. METHOD: As part of the process, an evidence-based framework used a common language to assess the efficacy of the respective nine programs. RESULTS: All nine programs are now mapped into the EBP framework. CONCLUSIONS: This review allows staff to become more intentional and informed about the EBPs they employ to support vulnerable families and to use this knowledge to better inform the families with whom they work. This paper and the process the agency followed can be a model for other organizations who serve families experiencing short-term housing crisis, provide infant nursery care, and other support services for families with young children.
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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 | 0.356 | 0.213 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.026 | 0.009 |
| Science and technology studies | 0.015 | 0.038 |
| Scholarly communication | 0.031 | 0.019 |
| Open science | 0.015 | 0.022 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".