Predicting high school teacher-coaches' job satisfaction
Bibliographic record
Abstract
In Canada, high school athletic programs rely on the efforts and initiatives taken by high school teachers. These teacher-coaches coach sports teams outside of their regular academic responsibilities, and volunteer to ensure the success of their school's athletic program. Previous research suggests that involvement in extra-curricular activities is associated with increased job satisfaction for teachers; however, limited research has examined specific aspects of the coaching experience and how these may impact teacher-coaches' job satisfaction. The purpose of this study is to examine high school teacher-coaches' impressions of the quality of their relationships with their athletes, as well as their self-efficacy towards coaching, and how this relates to their reported teaching satisfaction. The sample was comprised of 2949 teacher-coaches, representing all of the Canadian provinces and territories, who participated in a national survey on their experiences. The results showed that teachers who reported increased commitment, closeness, and complementary with their athletes (coach-athlete relationship questionnaire) and increased self-efficacy towards coaching in terms of developing motivation, game strategy, technique, character, and physical conditioning (Coaching Efficacy Scale), reported higher teacher satisfaction (Teacher Satisfaction Scale). The model was tested using structural equation modeling and had a good fit (?2 (63) = 614.08, p < .001, SRMR = .044, CFI = .967, TLI = .959, RMSEA = .054 CI95 [.050, .058]). Alternative models were also explored and invariance testing for gender and teaching subject was also conducted. Overall, the results support that positive coach-athlete relationships, along with self-efficacy for coaching, predict increased teacher satisfaction.Acknowledgments: This research was conducted in with the assistance of an Insight Development Grant from SSHRC and Sport Canada through the Sport Participation Research Initiative.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".