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Record W4206034247 · doi:10.1177/16094069211070408

But is It Hermeneutic <i>Enough</i> ?: Reading for Methodological Salience in a Scoping Review of Hermeneutics and Implementation Science

2022· review· en· W4206034247 on OpenAlexaff
Graham McCaffrey, Erin Wilson, Steinunn Jónatansdóttir, Lela Zimmer, Peter Zimmer, Ian D. Graham, David Snadden, Martha MacLeod

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2022
Typereview
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsOttawa HospitalUniversity of OttawaPositive Living NorthUniversity of Northern British ColumbiaUniversity of Calgary
Fundersnot available
KeywordsHermeneuticsEpistemologyRelevance (law)SalientSalience (neuroscience)Situational ethicsAlterityDialecticSociologyPsychologyComputer sciencePhilosophyPolitical scienceCognitive psychology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.047
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.867
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0470.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.880
GPT teacher head0.815
Teacher spread0.065 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2022
Admission routes1
Has abstractyes

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