Evaluating Youth Drop-In Programs: The Utility of Process Evaluation Methods
Bibliographic record
Abstract
Abstract: In North America, neighbourhood youth centres typically offer essential community-based programs to disadvantaged and marginalized populations. In addition to providing pro-social and supportive environments, they provide a host of educational and skill-development opportunities and interventions that build self-esteem, increase positive life relationships and experiences, and address social determinants of health. However, evaluators of such centres often have to work with moving changes in temporal components (i.e., service users, services, programs, and outcomes) that are unique and idiosyncratic to the mandate of the centre. Although there is an abundance of research on youth programs in general, there is a void in the literature on drop-in programs specifically, which this study aims to address. The lack of empirical research in this area inhibits knowledge about the processes of these centres. For this reason, the article concludes that process evaluation methods may be effectively used to substantiate the practice skills, knowledge, and managerial competencies of those responsible for program implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".