Analyzing a Sport for development program’s logic model with key actors’ \nperceptions: The case of Pour 3 Points organization in Montreal
Bibliographic record
Abstract
More work is needed on measuring the impact of Sport for Development (SFD) organization and on the managerial structures and processes for change. The purpose of the current study was to analyze the logic model (LM) of a SFD program in Canada that provides training for high school coaches in low socioeconomic communities in Montreal. Methods: Key actors (i.e., coaches, program administrators, school directors, and sport coordinators; N=22) were interviewed about their perceptions of the different components of the organization’s LM, namely the program’s context, the initial problem it addressed, its needs, objectives, input, output, and impacts. Findings: Findings reveal the participants perceived the program as being successful by all key actors. Participants had similar understandings regarding the targeted problem and context, but their views differed regarding their understanding of the program’s activities. In addition, the key actors made suggestions to improve the program, including clarifying its objectives, reinforcing internal communication, and building stronger partnerships with the partner schools. Conclusions: Findings from the present study provide recommendations to help improve the organization’s LM. In addition, these findings can help researchers and SFD administrators reinforce essential organizational program structures and activities for better management, evaluation, and improved impact on communities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".