Using the Learning Climate Questionnaire to Assess Basic Psychological Needs Support in Youth Sport
Bibliographic record
Abstract
Basic psychological needs are underlying mechanisms that can contribute to psychosocial development and well-being within youth sport. The purpose of this article was to validate the Learning Climate Questionnaire (LCQ) as a measure of basic psychological needs support within this context. Study 1 (n = 445) examined the validity and factor structure of Standage et al.’s (2005) 24-item LCQ using a 7-point Likert scale, as a measure of assessing autonomy support, competence support, and relatedness support in the youth sport context. In Study 2, the validity and reliability of the scale continued to be evaluated by assessing the its fit in a different sample of youth (n = 253) using a 6-point Likert scale. Overall, results indicated that a 15-item self-report measure of autonomy support, competence support, and relatedness support is a valid and reliable tool that can be used in youth sport. The LCQ can be used to inform and evaluate intervention work with youth sport coaches, to test models, and to assess longitudinal change over the course of a sport program. Limitations and future research directions are discussed. Lay Summary: A measure of basic psychosocial support has not been validated in youth sport. Having such a tool would allow practitioners and researchers to monitor coaching practices, evaluate interventions, and test theories. This two-part study validated a 15-item measure that can effectively assess basic psychological needs support in this context.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".