Using the Delphi Method for Qualitative, Participatory Action Research in Health Leadership
Bibliographic record
Abstract
Current pressures on public health systems have led to increased emphasis on restructuring, which is seen as a potential solution to crises of accessibility, quality, and funding. Leadership is an important factor in the success or failure of these initiatives. Despite its importance, health leadership evades easy articulation, and its study requires a thoughtful methodological approach. We used a modified Delphi method in a Participatory Action Research (PAR) project on health leadership in Canada. Little has been written about the combination of Delphi method with PAR. We offer a rationale for the combination and describe its usefulness in researching the role of leadership in a restructuring initiative in “real time” with the participation of health system decision makers. Recommendations are provided to researchers wishing to use the Delphi method qualitatively (i.e., without statistical consensus) in a PAR framework while protecting the confidentiality of participants who work at different levels of authority. We propose a modification of Kaiser's (2009) post-interview confidentiality form to address power differentials between participants and to enhance confidentiality in the PAR process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.209 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".