Perceiving the team environment: A multilevel analysis of psychological climate and effort among female athletes
Bibliographic record
Abstract
Research has been devoted to better understanding how the perceived environment comes to manifest itself in the behavior of individuals (McGrath, 1984). Perceiving one’s environment as psychologically safe and meaningful (positive psychological climate) has been associated with a higher degree of personal engagement (James et al., 2008). In the sport setting, when male ice hockey players perceived a psychologically safe environment, engagement in the form of athlete-reported effort increased (Spink et al., 2013). Given that sex differences have emerged in extant climate literature (e.g., Carr et al., 2000), it was deemed important to further explore this relationship in a sample of female athletes. To do so, 180 participants (representing 16 teams) completed an adapted version of the Psychological Climate Questionnaire (PCQ; Brown & Leigh, 1996) and a 6-item perceived effort measure (Spink et al., 2013) following a late season practice. To account for nesting of players within teams (ICC = 0.08), a multilevel model used four subscales of the PCQ (supportive management, role clarity, self-expression, and contribution) to predict effort. Results revealed that two of the subscales (role clarity and contribution) significantly predicted effort (p < .001) accounting for 17.62% of the overall variance. This finding suggests that when female athletes perceive (a) a significant contribution to various team outcomes using their unique skill sets and (b) clear views surrounding role-related behaviors, they are more likely to report working hard. In relation to the study purpose, this pattern of results shared both similarities and differences with males. In terms of similarities, role clarity emerged as a predictor in both samples (Spink et al., 2013) whereas the climate subscales of self-expression emerged for males and contribution for females. While replication is needed, engagement in the form of self-reported effort appears to be associated with how both sexes perceive their team environment.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".