Relational aspects of building capacity in economic evaluation in an Australian Primary Health Network using an embedded researcher approach
Bibliographic record
Abstract
Abstract Background: Health organisations are increasingly implementing ‘embedded researcher’ models to translate research into practice. Against specified aims, this paper examines the impact of an embedded researcher model known as the embedded Economist (eE) Program that was implemented in an Australian Primary Health Network (PHN) located in regional New South Wales, Australia. The site, participants, program aims and design are described. Insights into the relational facilitators, challenges and barriers to the integration of economic evaluation perspectives into the work of the PHN are provided. Methods: The eE Program consisted of embedding a lead health economist on site, supported by off-site economists, part-time, for three- and three-quarter months to collaborate with PHN staff. Evaluation of the eE at the PHN included qualitative data collection via semi-structured interviews (N= 34), observations (N=8) and a field diary kept by the embedded economists. A thematic analysis was undertaken through the triangulations. Results: The eE Program successfully met its aims of increasing PHN staff awareness of the value of economic evaluation principles in decision making and their capacity to access and apply these principles. There was also evidence that the program resulted in PHN staff applying economic evaluations when commissioning service providers. Evaluation of the eE identified two key facilitators for achieving these results. First, a highly receptive organisational context characterised by a work ethic, and site processes and procedures that were dedicated to improvement. Second was the development of trusted relationships between the embedded economist and PHN staff that was enabled though: the commitment of the economist to bi-directional learning; facilitating access to economic tools and techniques; personality traits (likeable and enthusiastic); and because the eE provided post-embedding support for PHN projects. Conclusions: This study provides the first detailed case description of an embedded health economics program. The results demonstrate how the process, context and relational factors of engaging and embedding the support of a health economist works and why. The findings reinforce international evidence in this area and are of practical utility to the future deployment of such programs. Trial registration: N.A.
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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 | 0.081 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".