Content Validation of a Recreational and Sport Risk-Taking Scale
Bibliographic record
Abstract
Purpose: The practice of sport and leisure has many physiological and psychological benefits. However, certain behaviours may contravene the physical integrity and well-being of participants, notably through sports injuries. Several endogenous (sensation seeking, risk perception, psycho- affective aspects, substance consumption, age) and exogenous (social influence, recreational and sporting factors, protective equipment, physical environment) dimensions make up risk-taking behaviours. Method: A qualitative study helped in developing an explanatory risk-taking model. A scale, based on the results of this work, could serve as a useful tool to better understand the determinants of leisure risk-taking among young people and, thus, propose more relevant prevention measures. The purpose of our research is to precisely design and attest to the content validity of a scale based on measuring recreational and sport risk-taking factors among young people between the ages of fourteen and twenty-four years through a Delphi survey with experts (n=7) and two focus groups (n=12). Results: Our findings show that, after these two data collections, the questionnaire displays satisfactory content validity. Continued analysis of psychometric qualities will ensure construct vali dity and fidelity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".