Portuguese Football Coaches’ Role in Facilitating Positive Development Within High Performance Contexts: Is Positive Development Relevant?
Bibliographic record
Abstract
Over the last decades positive development (PD) has served as a framework for several investigations within the sport science community. In fact, multiple researchers have analyzed youth coaches’ role in PD. However, there is recent interest in exploring high performance coaching due to the complexity of the coaching practice, the different developmental needs presented by players, and the relevance of PD within this particular environment. The purpose of this study was to understand the perspectives of Portuguese football coaches about the importance of PD in high performance coaching. The participants in the study were ten male Portuguese football coaches who trained athletes between the ages of 16 and 39 years of age. Findings showed that coaches viewed winning and on field performance as top priorities in their coaching philosophy, but recognized the importance of PD. Coaches also envisioned the determinant role youth coaches have in this domain. Coaches conceptualized PD as an overarching framework that could be used across the developmental spectrum to convey a range of PD outcomes in high performance contexts such as teamwork, respect for others and transfer to other life domains. Moving forward, coach education courses should help coaches develop strategies to foster PD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".