Modeling the Mental Health Practice Change Preferences of Educators: A Discrete-Choice Conjoint Experiment
Bibliographic record
Abstract
Schools are sometimes slow to adopt evidence-based strategies for improving the mental health outcomes of students. This study used a discrete-choice conjoint experiment to model factors influencing the decision of educators to adopt strategies for improving children’s mental health outcomes. A sample of 1,010 educators made choices between hypothetical mental health practice change strategies composed by systematically varying the four levels of 16 practice change attributes. Latent class analysis yielded two segments with different practice change preferences. Both segments preferred small-group workshops, conducted by engaging experts, teaching skills applicable to all students. Participants expressed little interest in Internet options. The support of colleagues, administrators, and unions exerted a strong influence on the practice change choices of both segments. The Change Ready segment, 77.1 % of the sample, was more intent on adopting new strategies to improve the mental health of students. They preferred that schools, rather than the provincial ministry of education, make practice change decisions, coaching was provided to all participants, and participants received post-training follow-up sessions. The Demand Sensitive segment (22.9 %) was less intent on practice change. They preferred that individual teachers make practice change decisions, recommended discretionary coaching, and chose no post-training follow-up support. This study emphasizes the complex social, organizational, and policy context within which educators make practice change decisions. Efforts to disseminate strategies to improve the mental health outcomes of students need to be informed by the preferences of segments of educators who are sensitive to different dimensions of the practice change process. In the absence of a broad consensus of educators, administrators, and unions, potentially successful practice changes are unlikely to be adopted.
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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 | 0.033 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".