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Record W1231914981 · doi:10.26443/ijwpc.v1i2.5

Narrative and Palliative Care Team Identity Formation

2014· article· en· W1231914981 on OpenAlexvenueno aff
Denise Hess

Bibliographic record

VenueInternational Journal of Whole Person Care · 2014
Typearticle
Languageen
FieldMedicine
TopicMedical and Biological Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeAllianceMeaning (existential)Palliative carePsychosocialContext (archaeology)Identity (music)PsychologyNursingFlourishingPsychotherapistMedicineAesthetics

Abstract

fetched live from OpenAlex

Palliative care is whole person care that attends to the physical, psychosocial, and spiritual needs of persons with a serious or life-limiting illness. This care is provided by a team of clinicians from several disciplines including physicians, nurses, social workers, and chaplains. The palliative care team functions as a dynamic system whose ability to provide quality care is dependent upon the ability of the team members to form and maintain an ongoing collaborative alliance. This alliance requires that team members maintain dual commitments to both the care receivers and to their fellow team members. Just as persons with illness express the human propensity toward meaning making in the face of suffering, so palliative care teams thrive when they are supported in reflective processes that enhance their ability to find meaning in their work. Creation of and attention to team narratives and their role in team identity formation can enhance team members’ flourishing by placing team identity in the context of a larger story. Narratives of rescuing and fixing foster a sense of control and expertise while narratives of containing and healing nurture attention to mindful presence and human-to-human encounter.

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)0.010
metaresearch head score (Gemma)0.025
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0080.014
Scholarly communication0.0080.008
Open science0.0010.010
Research integrity0.0020.003
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 head0.031
GPT teacher head0.334
Teacher spread0.303 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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