Measuring secondary school staff confidence and concerns around youth mental health, 2021
Bibliographic record
Abstract
This study collected data on secondary school staff confidence and worries around delivering mental health content to students. The aim of the study was to assess the psychometric properties of two new measures developed in Canada and previously tested on a sample of elementary school teachers (Linden &Stuart, 2019). A comprehensive mixed-methods survey was developed including the two measures in question: The Teacher Confidence Scale to Deliver Mental Health Content (TCS-MH) and the What Worries Me Scale (WWMS), as well as a measure of mental health knowledge (MHKS; Dooley et al 2014), mental health stigma (RIBS; Evans-Lacko et al, 2011), teacher efficacy (TSES; Tschannen-Moran & Woolfolk Hoy, 2001), and anxiety (DASS; Lovibond & Lovibond, 1995). The survey also included detailed demographic questions, capturing information both about the participants and their schools. Finally, the survey included an open question where participants were asked to describe their experience of addressing mental health in school. Participants were recruited via social media, via Jigsaw, the National Centre for Youth Mental Health in Ireland, and via snowball sampling. In total, n=644 secondary school staff members completed the survey. The majority of the final sample were female (n=514) and were subject teachers (n=444), although n=79 of these teachers also reported having an additional role. The final sample also included school leadership, staff in wellbeing support roles, learning support staff, and non-teaching staff. Less than half of the sample indicated that they had received previous mental health training (n=289) and details of this training was included in n=246 responses. N=359 participants also responded to the open question detailing their experience of addressing mental health in the course of their work. Data on the schools represented in the sample fit well with national figures from the Department of Education from the same year.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".