What Does It Take to Transform Mental Health Knowledge into Workplace Practice? Towards a Theory of Action
Bibliographic record
Abstract
The purpose of this discussion paper is to consider the question of a knowledge base that might undergird a systematic approach to transforming mental health knowledge into workplace practice. Drawing on a range of systems-change and related research, the paper begins by contrasting the nature of "workplace" as opposed to "mental health knowledge" systems. On the basis of the Luhmann's concepts, these systems are cast as fundamentally being determined by the types of communication in which they are engaged (business about business matters, and mental health about mental health matters). For information from one to be adopted by the other requires the translation of mental health concepts into language understood by the workplace, by people capable of understanding both. The paper then examines the importance of determining a vision of desired outcome from such knowledge transfer. What is the desired outcome? A workplace free of mental health problems? The role of both values and existing knowledge in determining these is outlined, along with the importance of engaging the most immediately involved actors in developing the vision. Because valid and reliable knowledge is central to the task, a framework is set out in which the most immediately relevant research findings might be considered in relation to each other. Three categories of knowledge are proposed: employer-led preventive measures, employee-focused workplace interventions and community-based resources supportive of workplaces. Knowledge drawn from summative analyses of existing research is matched against these three categories to illustrate the potential for guiding both implementation and research decision making.The final section draws together the key elements of an active approach to promoting and supporting knowledge transformation from mental health research to workplaces.
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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 | 0.050 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.010 | 0.098 |
| Scholarly communication | 0.029 | 0.032 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".