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Record W2334773739 · doi:10.3109/13561820.2015.1115395

Interprofessional team building in the palliative home care setting: Use of a conceptual framework to inform a pilot evaluation

2016· article· en· W2334773739 on OpenAlexaff
James Shaw, Colleen Kearney, Brenda Glenns, Sandra McKay

Bibliographic record

VenueJournal of Interprofessional Care · 2016
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsToronto Rehabilitation Institute
Fundersnot available
KeywordsPalliative careNursingInterprofessional educationConceptual frameworkMedicinePsychologyMedical educationHealth careSociologyPolitical science

Abstract

fetched live from OpenAlex

Home-based palliative care is increasingly dependent on interprofessional teams to deliver collaborative care that more adequately meets the needs of clients and families. The purpose of this pilot evaluation was to qualitatively explore the views of an interprofessional group of home care providers (occupational therapists, nurses, personal support work supervisors, community care coordinators, and a team coordinator) regarding a pilot project encouraging teamwork in interprofessional palliative home care services. We used qualitative methods, informed by an interprofessional conceptual framework, to analyse participants' accounts and provide recommendations regarding strategies for interprofessional team building in palliative home health care. Findings suggest that encouraging practitioners to share past experiences and foster common goals for palliative care are important elements of team building in interprofessional palliative care. Also, establishing a team leader who emphasises sharing power among team members and addressing the need for mutual emotional support may help to maximise interprofessional teamwork in palliative home care. These findings may be used to develop and test more comprehensive efforts to promote stronger interprofessional teamwork in palliative home health care delivery.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.079
GPT teacher head0.482
Teacher spread0.403 · 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

Citations20
Published2016
Admission routes1
Has abstractyes

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