An Australian Example of Translating Psychological Research into Practice and Policy: Where We are and Where We Need to Go
Bibliographic record
Abstract
Research findings from psychological science have identified interventions that will benefit human health. However, these findings are not often incorporated into practice-based settings or used to inform policy, in part, due to methodological and contextual limitations. A strategic approach is required if we are to find a way to facilitate the translation of these findings into areas that will offer genuine impact on health. There is an overwhelming focus on conducting more clinical trials, without consideration of how to ensure that findings from such trials make it to the patients or populations for whom they were intended. The aim of this paper is to outline how the Black Dog Institute, an Australian medical research institute, has created a framework designed to facilitate the translation of research findings into practice-based community settings, and how these findings can be used to inform policy. We propose that the core strategies adopted at the Black Dog Institute to prioritize and implement a translational program will be useful to institutes and organizations worldwide to augment the impact of their work. We provide several examples of how our research has been implemented in practice-based settings at a community-level, and how we have used research in psychology as a platform to inform policy change.
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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 | 0.044 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".