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Record W4366601931 · doi:10.1080/10410236.2023.2198673

Team Care for the Care Team: A Scoping Review of the Relational Dimensions of Collaboration in Healthcare Contexts

2023· review· en· W4366601931 on OpenAlexafffund
Stéphanie Fox, Kirstie McAllum, Laura Ginoux

Bibliographic record

VenueHealth Communication · 2023
Typereview
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et Culture
KeywordsCLARITYHealth careTeamworkCompassionPsychologyTeam effectivenessTeam compositionNursingKnowledge managementMedicineSocial psychologyManagementPolitical science

Abstract

fetched live from OpenAlex

Examining team care for the care team, this scoping literature review highlights the relational and compassionate dimensions of collaboration and teamwork that can alleviate healthcare worker suffering and promote well-being in challenging contexts of care. Its goal is to provide greater conceptual clarity about team care and examine the contextual dimensions regarding the needs and facilitators of team care. Analysis of the 48 retained texts identified three broad types of communicative practice that constitute team care: sharing; supporting; and leading with compassion. The environmental conditions facilitating team care included a caring team culture and specific and accessible organizational supports. These results are crystallized into a conceptual model of team care that situates team care within a system of team and organizational needs and anticipated outcomes. Gaps in the literature are noted and avenues for future research are suggested.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.056
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0220.027
Science and technology studies0.0020.002
Scholarly communication0.0050.005
Open science0.0020.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.159
GPT teacher head0.572
Teacher spread0.414 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations17
Published2023
Admission routes2
Has abstractyes

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