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Record W4417327850 · doi:10.1080/10410236.2025.2599481

Shared Care, Valued Knowledge: How Family Caregivers and Healthcare Workers Negotiate Hybrid Caregiving Expertise through Relational Collaboration

2025· article· en· W4417327850 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueHealth Communication · 2025
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversité de Montréal
FundersMinistère de la SantéMinistère de la Santé et des Services sociaux
KeywordsDialogicNegotiationGeneral partnershipNarrativeHealth careExperiential learningQuality (philosophy)

Abstract

fetched live from OpenAlex

By analyzing two narratives of encounters between family caregivers from minority ethnocultural groups and healthcare professionals, this paper explores the communicative and collaborative challenges involved in integrating diverse forms of caregiving expertise in healthcare, particularly in contexts where task-based medical expertise is prioritized over relational, experiential expertise. We then illustrate how a dialogic approach to caregiving partnership can support both healthcare workers and family caregivers, especially those from minority backgrounds, in building quality relationships and co-constructing a hybrid caregiving expertise that merges task-oriented and relationship-centered knowledge. For such partnerships to emerge, interactional partners must strengthen their relational collaboration skills by demonstrating respect, cultural humility, compassion, and trust. This kind of relational work provides the impetus for moving beyond fragmented care and creating more culturally appropriate care trajectories.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.645
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.001
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 head0.054
GPT teacher head0.438
Teacher spread0.384 · 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