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Record W1479861538 · doi:10.1177/160940691501400102

Evidence-Informed Primary Bereavement Care: A Study Protocol of a Knowledge-to-Action Approach for Systems Change

2015· article· en· W1479861538 on OpenAlexaff
Ariella Lang, Andrea R. Fleiszer, Fabie Duhamel, Megan Aston, Tracy Carr, Sharon Goodwin

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2015
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsDalhousie UniversityMcGill UniversityUniversity of New BrunswickUniversité de MontréalVictorian Order of Nurses
Fundersnot available
KeywordsGeneral partnershipAction (physics)Process (computing)Context (archaeology)Action researchKnowledge managementAction planProcess managementData collectionProtocol (science)GuidelineKnowledge baseComputer sciencePsychologyMedicineEngineeringBusiness

Abstract

fetched live from OpenAlex

Using a systems change approach to knowledge-to-action, the purpose of this study is to increase organizational and practitioner uptake of an evidence-informed primary bereavement care guideline in home and community care through a researcher-knowledge-user partnership. Guided by the Consolidated Framework for Implementation Research, a single case study design will be used to examine system change in its natural context. The project integrates an organizational change initiative and a research study. Multiple data collection and analysis strategies will be used to explore and map the interactive synergistic process of interconnected decisions and actions. Iterative cycles of analysis and feedback will incite knowledge-user reflection and action, shape and substantiate findings, use data in a timely fashion to inform and guide next steps, and ensure ongoing monitoring and evaluation of the 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 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)0.013
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.134
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.880
GPT teacher head0.739
Teacher spread0.141 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreMethods

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

Citations4
Published2015
Admission routes1
Has abstractyes

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