Participation and satisfaction with thee-learning course “Mental health promotion in school health care”: a mixed-method study
Bibliographic record
Abstract
Purpose This study aims to evaluate the participation and satisfaction of learners with the brief “Mental health promotion in school health care” e-learning course, and to describe factors related to their participation and satisfaction. Design/methodology/approach A convergent mixed-method study with a descriptive, posttest-only design was conducted in Finland. Quantitative data was collected from the learning portal data and via an electronic feedback questionnaire and qualitative data in four focus group discussions. Findings The three modules of the course were opened altogether 12,922 times during the 21 months period. The identified factors influencing participation within the course were: favorite methods attracting attention, the relevance of additional information and postprocessing boosting diligence. The learners’ satisfaction with the course was high. The factors identified for improving satisfaction were: filling gaps in earlier education, clear and concise structure and content, inspiring and interesting design and suitability for clinical use. In addition to the primary target group (school nurses), the course was well-participated and evaluated as satisfying among other health and social care professionals as well as undergraduate students. Originality/value This study demonstrates demand for brief, fully online mental health trainings among school nurses, but also among other health and social care professionals and students. The results indicate that learners perceive such training as beneficial. This paper also presents a novel training intervention and its pedagogical base.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".