Predicting self-efficacy to teach comprehensive sex education
Bibliographic record
Abstract
Decades of research regarding the provision of comprehensive sex education (CSE) have resulted in calls for teachers to receive more and better training to teach this critical content. However, little is known about current CSE training practices in education programs and the extent to which they develop self-efficacy to teach CSE. The authors surveyed 134 preservice teachers in Eastern Canadian Bachelor of Education programs. The majority of participants reported receiving no sex education training whatsoever during their degree. Participants who did receive training reported that it was minimal and part of elective, not mandatory, coursework. Many preservice teachers expressed disappointment about this gap in their training. A hierarchical multiple regression analysis found that individual differences in comfort with sexual topics predicted self-efficacy to teach sex education, suggesting that teacher comfort should be a key target for training preservice teachers. Although these results support the need for mandatory preservice training, they also contribute to growing evidence that teachers with higher comfort with sexual topics should be supported in pursuing self-directed learning as a more immediate way to enhance their effectiveness in the provision of comprehensive sex education in Canadian classrooms.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".