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Record W4311286441 · doi:10.1111/phn.13153

Building consensus in defining and conceptualizing acceptability of healthcare: A Delphi study

2022· article· en· W4311286441 on OpenAlexaff
Joy Blaise Bucyibaruta, Mmapheko Doriccah, Lesley Bamford, Annatjie van der Wath, Thomas A. Dyer, Andrea Murphy, Paul Gatabazi, Rafiat Anokwuru, Innocent Muhire, Clarissa Anna Coetzee, Helene Coetzee, Alfred Musekiwa

Bibliographic record

VenuePublic Health Nursing · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsDelphi methodHealth careDelphiContext (archaeology)Thematic analysisConceptual frameworkConstruct (python library)Quality (philosophy)PsychologyNursingKnowledge managementManagement scienceMedicineComputer scienceSociologyQualitative researchPolitical scienceEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: The concept of healthcare acceptability is important for nursing staff spending most of their time with patients. Nevertheless, acceptability remains confusing without a collective definition in existing literature. OBJECTIVE: This study aimed to create a consensus among experts on definition and conceptual framework of healthcare acceptability. METHODS: We conducted two rounds of Delphi surveys to collect opinions from experts on definition and conceptual framework of healthcare acceptability proposed following thematic content analysis. We calculated the consensus among experts using the modified Appraisal of Guidelines for Research & Evaluation II (AGREE II) instrument and followed the guidance on conducting and reporting Delphi studies (CREDES) best practices. RESULTS: A total of 34 experts completed two rounds of Delphi survey. The definition was validated through consensus as: "a multi-construct concept describing the nonlinear cumulative combination in parts or in whole of experienced or anticipated specific healthcare from the relevant patients/participants, communities, providers/researchers or healthcare systems' managers and policy makers' perspectives in a given context." The overall quality rating was 92.6% and 95.1% for the proposed definition and conceptual framework respectively. CONCLUSION: Opinions collected from experts provided significant insights to build a consensus on healthcare acceptability advancing public health nursing.

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)0.020
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.263
GPT teacher head0.528
Teacher spread0.265 · 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; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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

Citations13
Published2022
Admission routes1
Has abstractyes

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