But is It Hermeneutic <i>Enough</i> ?: Reading for Methodological Salience in a Scoping Review of Hermeneutics and Implementation Science
Bibliographic record
Abstract
Hermeneutic methods have been widely used in health research. Through conducting a scoping review of hermeneutic studies related to implementation in healthcare, we identified various approaches and common strengths across studies. The review was part of a larger study exploring how hermeneutics could contribute fresh perspectives to implementation science. We looked at a large number of studies that reported some use of hermeneutics with a focus on what they had to say about processes of implementation in health care environments. While meeting our primary goal of identifying what was salient to implementation, we came up against the question of what made for a strong hermeneutic study. Through an extensive process of evaluation and discussion, several common elements emerged across studies that used hermeneutics: participatory conversations, reflective spaces, attention to alterity, and close-up granular detail. In this article, we outline the review process, then focus on six articles that met our criteria for relevance to implementation and hermeneutic strength. We discuss how some or all the common elements appeared in the articles, despite wide variations in topic and in how hermeneutics was applied. We argue that strength in hermeneutic research stems from a dialectic between applied principles and outcomes.
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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 | 0.442 | 0.740 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.027 | 0.029 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.031 | 0.038 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".