The Successful Transitions Initiative: Youth Skills Development Programming and How At-risk High School Participants Report Impact to Academic Success and Dropout Outcomes
Bibliographic record
Abstract
People who fail to attain a high school education face a multitude of risk factors associated with health, employment, crime, substance abuse, and their personal lives. The Successful Transitions Initiative on Prince Edward Island, a unique program targeting youth at-risk of dropping out of high school, teaches the youth basic skills in hopes of improving their academic outcomes. Skills development programs with similar mandates and curriculum are growing in popularity across Canada. It is important that the impact of these programs be understood to ensure they are affecting the participants positively and to improve program delivery. A case study was completed to understand the impact of the Successful Transitions Initiative. Pre and post-test survey data and post intervention focus group data were analysed to measure the 2017 Successful Transitions Initiative participant-reported outcomes of the program. The participants reported improved communication skills, pride and a sense of accomplishment, higher levels of selfconfidence, self-actualization, internal locus of control, increased school engagement, and newly formed positive relationships. The Successful Transitions Initiative case study also revealed trends that support a relationship between the magnitude and the longevity of the effect. Limitations of this study unearthed recommendations for future research designs and methods.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".