Bibliographic record
Abstract
There is a symbiotic relationship between qualitative research and knowledge mobilization. However, there is often a significant lag between the conduct of research and the findings reaching the public (including those it impacts and those with the power to make change). In designing my study on ethics and policy in long-term care (LTC) with the aim of improving quality care, I sought a method that would not only generate broad data but data on which stakeholders would be able to more readily act and thus promote meaningful and timely change. With this goal in mind, I adopted an empirical ethics approach with the aim of consensus building and used an innovative qualitative Delphi method to explore ethics and ethical reasoning in LTC and the interaction between frontline practice and organizational and public policy. While the Delphi method is starting to gain more recognition alongside more well-known qualitative methods, it has yet to be used in the exploration of empirical ethics and policy in LTC. The rich findings from this study suggest it has the potential of expanding the breadth of qualitative inquiry, as it draws on real-world practice and decision-making to promote consensus in areas of organizational, policy, and practice change. There is a need for further research using a modified qualitative Delphi method in empirical ethics and policy research in LTC, as well as other areas. However, the results of this study suggest the suitability and benefits of the approach for qualitative inquiry and propose researchers seeking to expand research findings, knowledge mobilization, and to connect findings to meaningful and practical change should consider adopting the Delphi method.
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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 | 0.230 | 0.353 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.013 | 0.053 |
| Scholarly communication | 0.041 | 0.038 |
| Open science | 0.006 | 0.048 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".