Girls just wanna have fun: Understanding the impact of a female youth-driven physical activity-based life skills program
Bibliographic record
Abstract
Positive youth development emerged as a proactive approach to help youth develop into healthy adults through the acquisition of psychosocial skills (Damon, 2004; Holt & Jones, 2008). Female youth from families living on low incomes are at highest risk for poor developmental outcomes and have the lowest levels of physical activity (PHAC, 2006; Wilson et al., 2005). One program currently being implemented in Ottawa designed to help youth develop life skills in order to succeed and develop into healthy adults is the Girls Just Wanna Have Fun (GJWHF) program. The purpose of this research was to examine whether the program is perceived as helping female youth develop life skills and whether these life skills are being transferred into other life domains (e.g., school, family). This research used a mixed methods approach and represents an important step in responding to calls for increased evaluation of community-based programs (Salmon et al., 2007) and enhanced understanding of how life skills can be developed and transferred to other life domains (Samuels, 2006; Theokas et al., 2008). Results indicated that the program was successful in facilitating the development of life skills (teamwork, leadership, confidence, communication, respect) and aiding youth transfer such skills to other environments; however it was observed that more deliberate structure is needed within the program to further facilitate the transference of these skills.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".