Gender Differences in Stressors and Coping Strategies Among Teacher Education Students at University of Ghana
Bibliographic record
Abstract
This study explored gender differences in stressors experienced by teacher education students at the University of Ghana, and adaptation stratagems they might utilise to manage stress. In 2018–2019 academic year, a total of two hundred and seventy (270) second- and third-year students were selected using random sampling procedure to respond to closed-ended and open-ended questions in a survey questionnaire. The questionnaire was adapted from Dental Environmental Stress (DES) to measure stressors students encounter and the Brief Coping Orientation to Problems Experienced (Brief COPE) to measure students’ coping stratagems they might use to minimise their stress levels (Folkman & Lazarus, 1984). It was pre-tested to learners of the faculty of education at the University of Cape Coast, Ghana, to ensure the reliability and validity of the statements. The findings show that the students use multiple strategies, such as praying/meditating and self-distracting activities to cope with stress. Although, females had higher overall perceived stress levels regarding encountered academic stressors and health stressors, the difference between genders was insignificant. Similarly, females had a higher perception of stress from psychosocial stressors when likened to males, however, the difference between genders was also insignificant. Regarding perceived coping stratagems, females utilised adaptive coping stratagems whilst males utilised maladaptive and avoidance coping stratagems although the difference between genders was also not significant. The study recommended among others that males be urged to likewise utilise increasingly adaptive strategies to control strain.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".