Effects of a Health Education Course on Pre‐Service Teachers' Perceived Knowledge, Skills, Preparedness, and Beliefs in Teaching Health Education
Bibliographic record
Abstract
ABSTRACT BACKGROUND In this study, we explore how a health education course may play a role in pre‐service teachers' perceptions in teaching and integrating health education activities to nurture K‐8 students' health literacy. METHODS We used mixed methods to examine the effect of a health education course in a teacher education program. Of 55 pre‐service teachers, 41voluntarily participated in the study. Quantitative data were obtained through an online questionnaire administered to participants at the beginning and end of the course. We conducted 6 focus groups at the end of the course. RESULTS The inferential analysis from a series of analysis of variance with repeated measures revealed significant differences in health knowledge (F = 113.39, p < .01, η2 = 0.74), preparedness (F = 104.74, p < .01, η2 = 0.73), attitudes (F = 15.02, p < .01, η2 = 0.28), and beliefs (F = 8.87, p < .01, η2 = 0.19) between time points. Qualitative data led to the conclusion that where one health education course is insufficient, such a course is the first step into future curriculum development and implementation. CONCLUSION One health education course might be beneficial for general education teachers to increase their knowledge and preparation to teaching school health. On‐going training is needed for program success.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".