Exploring Basic Needs, Motivation, and Retention Among Female Sport Officials
Bibliographic record
Abstract
Sport officials in general, and female sport officials specifically, are underrepresented in the research. More work is required to better understand what attracts female sport officials to the role, along with what facilitates their retention. The purpose of this study was to examine the relationships between female sport officials’ motivations, basic needs, and intentions to remain as officials. Through an online survey, 186 female sport officials responded to (a) the Basic Needs Satisfaction in Sport Scale (BNSSS), (b) the Referee Retention Scale (RRS), and (c) questions assessing Reasons for Becoming Officials. Pearson correlation tests established relationships among various subscales, and regression tests were conducted to determine whether any variables predicted RRS scores. All five BNSSS subscales significantly correlated with most RRS subscales and one Reasons for Becoming Officials subscale. Further, regression analysis revealed that increased scores on the BNSSS—specifically feelings of competence, choice, volition, and relatedness—predicted intentions to remain as officials, as measured by the RRS. Since the BNSSS predicts retention, sporting organizations should implement retention strategies that focus on building competence, volition, and relatedness among female sport officials.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".