Secondary school teachers’ experiences of supporting students with mental health issues: a systematic review and meta-aggregation of qualitative studies
Bibliographic record
Abstract
Background: Mental health issues in adolescents is steadily increasing, mainly since the rise of social media, the concomitant effects of internet use and other problems associated with academic performance, familial abuse and bullying. Secondary school teachers are exposed to a wide range of adolescent behaviours, yet depression and anxiety appear to be those behaviours that are causing the greatest concern. Aim: This review aimed to appraise and synthesise qualitative studies examining secondary school teachers' experiences of supporting students experiencing mental health issues. Methods: Between January 2007 and March 2022, a search was conducted across nine databases, namely Medline, CINAHL Complete, APA PsycInfo, Embase, ERIC, Web of Science, Scopus, CNKI (for Chinese sources), and Wanfang (also for Chinese sources). This review focused on qualitative studies and included, but was not exclusive to, phenomenology, grounded theory, ethnography, action research, and feminist research. The selection of studies adhered to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The JBI approach was utilised for data extraction, synthesis, and critical evaluation. Additionally, the ConQual approach was employed to assess the reliability of the findings. Findings: A total of 2516 papers were identified, but only eleven qualitative studies were selected for inclusion in this review. Based on the JBI critical appraisal checklist for qualitative research, these studies were rated as having moderate quality. The studies originated from various countries, including three from the USA, one from the UK, Canada, Italy, Norway, Ireland, South Lebanon, Kenya, and China. A common theme identified in these studies was the challenges teachers faced when supporting students with mental health issues. Five categories emerged from the papers reviewed: ' Experiencing the Challenges of mental health issues in the Classroom', 'Teachers' understanding of mental health issues', 'Feeling at a loss and coping with compassion', 'Providing a supportive and close relationship beyond that of the role', 'Understanding the Conflicts Imposed on and by the Education System'. Conclusion: It is evident from this review that secondary school teachers experience difficulties and challenges in supporting students with mental health issues. This also has implications for school nurses who may be best placed to support both students and teachers alike. It is also recommended that further research be conducted in Asian communities because of the paucity of published work in this setting.
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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 | 0.061 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.027 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".