Professionals’ and Students’ Perceived Needs for an Online Supportive Application for Reducing School Absence and Stimulating Reintegration: Concept Mapping Study
Bibliographic record
Abstract
BACKGROUND: To limit students' medical absenteeism and premature school dropout in the Netherlands, the Medical Advice for Sick-reported Students (MASS) intervention was developed to enhance collaboration between students, parents, school, and health care professionals. MASS reduces medical absenteeism. However, it does not yet optimally support professionals in monitoring students nor automatically stimulating students' autonomy regarding their situation. OBJECTIVE: This study aimed to identify professionals' and students' perceived need for an online supportive application to monitor and reduce absenteeism and stimulate student autonomy and school reintegration. METHODS: Concept mapping sessions were held with professionals (n=23) and secondary school students (n=27) in group meetings or online to identify their perspectives and needs. Multidimensional scaling and hierarchical clustering were done with Ariadne 3.0 software. The resulting concept maps were reclustered and interpreted by 4 researchers. RESULTS: Three heterogeneous groups of professionals generated 17 clusters (135 unique statements), with a mean importance rating ranging from 2.9 to 4.6 on a Likert scale with scores ranging from 1 to 5. Three heterogeneous groups of secondary school students generated 18 clusters (95 unique statements), with a mean importance rating ranging from 3.2 to 4.6. Professionals considered as most important the following: easily accessible contact with students; supporting, motivating, and rewarding students; monitoring absent students; providing information to students and their parents; exchanging information between professionals. Students considered as most important the following: better teacher-student communication and respect; communication between school professionals on the one hand and parents, other professionals, and students on the other hand; guidance in missed learning materials and tests. Students perceived an online format for support as the obvious option. CONCLUSIONS: Both professionals and students were positive about an online application to support students in dealing with medical absenteeism, especially considering the need for better and easily accessible contact between students and professionals. An eHealth or mobile health (mHealth) application addressing these aspects could stimulate student autonomy and have positive effects on medical absenteeism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".