Evaluating a Brazilian online parent education programme in sport using the RE-AIM framework
Bibliographic record
Abstract
This study aimed to assess the effectiveness of a large-scale parenting programme on Brazilian tennis parents and sports administrators using the RE-AIM framework. The Tennis Parent Development Program was created as a Massive Open Online Course (MOOC), providing 11 free open-access modules on a web-based platform. A total of 122 parents took part in the programme. Interviews were conducted with eight parents and two sports administrators about their perceptions of the programme, and the data was analysed using reflexive thematic analysis. According to the data, parents and sports administrators found the online programme very convenient as it easily fit into parents' daily routines, making it easier to engage with. Although parents found the online resources to help inform them about their involvement in sports, there was a need to develop interaction activities and content that addressed the specific needs of players to expand the audience of parents. As such, it is important to design programmes that are less instructional and more applicable to a particular parent's situation in sports. This will help ensure that such programmes can complement other support activities for parents in sports organisations, such as face-to-face meetings. Additionally, to enhance the programme's effectiveness, it is recommended that fewer modules are provided, and the total duration of the programme is reduced. Furthermore, the lack of interaction opportunities between parents in online programmes was identified as an area for improvement. In conclusion, the Tennis Parent Development Programme is a useful resource for supporting parents.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".