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Record W3162092344 · doi:10.5737/23688076312205212

Incorporating reflective writing & art therapy in my palliative care practice

2021· article· en· W3162092344 on OpenAlexaffvenue
Kalliopi Stilos, Katherine Burgoyne

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsFeelingPsychosocialPalliative careAnxietyMedicineNursingDeliriumHealth professionalsEmotional supportHealth carePsychologyPsychotherapistPsychiatrySocial supportSocial psychology

Abstract

fetched live from OpenAlex

The specialty of palliative care routinely focuses on the complex needs of patients living with incurable illness and their families’ emotional and psychosocial concerns. Healthcare professionals who work with patients with advanced illness sometimes suffer from frustration and anxiety when they return home from caring for dying patients. The psychosocial care that increases patient and family satisfaction is sometimes lost when nurses are suffering (Pendry, 2007; Freeman, 2013). Continuous exposure to such difficult situations and the accumulation of unrecognized feelings and attitudes can lead to physical and psychological challenges (Pereira et al., 2011). As such, nurses have a duty to maintain their health to the best of their ability. To encourage nurses in promoting emotional health, Freeman’s (2013) CARES tool (Comfort, Airway management, Restlessness and delirium, Emotional and spiritual support, and Self-care) was integrated into our organization’s Comfort Measures Order Set for imminently dying patients (prognosis <72 hours) (Stilos, Wynntchuk et al., 2016).

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)0.011
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0050.003
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.003

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 head0.093
GPT teacher head0.392
Teacher spread0.299 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes2
Has abstractyes

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