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Record W2564488067 · doi:10.1080/15524256.2016.1247771

Advancing Hospice and Palliative Care Social Work Leadership in Interprofessional Education and Practice

2016· article· en· W2564488067 on OpenAlexaff
Susan Blacker, Barbara Head, Barbara L. Jones, Stacy S. Remke, Katherine Supiano

Bibliographic record

VenueJournal of Social Work in End-of-Life & Palliative Care · 2016
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsSocial workInterprofessional educationPalliative careNursingBest practiceWork (physics)MedicineHealth careMedical educationPolitical science

Abstract

fetched live from OpenAlex

The importance of interprofessional collaboration in achieving high quality outcomes, improving patient quality of life, and decreasing costs has been growing significantly in health care. Palliative care has been viewed as an exemplary model of interprofessional care delivery, yet best practices in both interprofessional education (IPE) and interprofessional practice (IPP) in the field are still developing. So, too, is the leadership of hospice and palliative care social workers within IPE and IPP. Generating evidence regarding best practices that can prepare social work professionals for collaborative practice is essential. Lessons learned from practice experiences of social workers working in hospice and palliative care can inform educational efforts of all professionals. The emergence of interprofessional education and competencies is a development that is relevant to social work practice in this field. Opportunities for hospice and palliative social workers to demonstrate leadership in IPE and IPP are presented in this article.

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)0.030
metaresearch head score (Gemma)0.033
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: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0080.007
Scholarly communication0.0090.006
Open science0.0020.019
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0060.001

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 head0.095
GPT teacher head0.468
Teacher spread0.374 · 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
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

Citations44
Published2016
Admission routes1
Has abstractyes

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