Designing a knowledge transfer and exchange strategy for the Alberta Depression Initiative: contributions of qualitative research with key stakeholders
Bibliographic record
Abstract
BACKGROUND: Depressive disorders are highly prevalent and of significant societal burden. In fall 2004, the 'Alberta Depression Initiative' (ADI) research program was formed with a mission to enhance the mental health of the Alberta population. A key expectation of the ADI is that research findings will be effectively translated to appropriate research users. To help ensure this, one of the initiatives funded through the ADI focused specifically on knowledge transfer and exchange (KTE). The objectives of this project were first to examine the state of the KTE literature, and then based on this review and a set of key informant interviews, design a KTE strategy for the ADI. METHODS: Face to face interviews were conducted with 15 key informants familiar with KTE and/or mental health policy and programs in Alberta. Interviews were transcribed and analyzed using the constant comparison method. RESULTS: This paper reports on findings from the qualitative interviews. Respondents were familiar with the barriers to and facilitators of KTE as identified in the existing literature. Four key themes related to the nature of effective KTE were identified in the data analysis: personal relationships, cultivating champions, supporting communities of practice, and building receptor capacity. These recommendations informed the design of a contextually appropriate KTE strategy for the ADI. The three-phased strategy involves preliminary research, public workshops, on-going networking and linkage activities and rigorous evaluation against pre-defined and mutually agreed outcome measures. CONCLUSION: Interest in KTE on the part of ADI has led to the development of a strategy for engaging decision makers, researchers, and other mental health stakeholders in an on-going network related to depression programs and policy. A similarly engaged process might benefit other policy areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".