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Record W4404470072 · doi:10.1097/njh.0000000000001069

Weathering the Storm

2024· article· en· W4404470072 on OpenAlexaff
Janice Nesbitt

Bibliographic record

VenueJournal of Hospice and Palliative Nursing · 2024
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsWinnipeg Regional Health Authority
Fundersnot available
KeywordsPalliative careCompassionMindfulnessCompassion fatigueNursingHealth careScale (ratio)Quality of life (healthcare)PsychologyMedicineBurnoutClinical psychologyPolitical science

Abstract

fetched live from OpenAlex

The provision of palliative care has been seen to have a significant impact on the interprofessional health care team. Being emotionally present to support patients and families at their most stressful points in life can result in compassion satisfaction or compassion fatigue. Providing palliative care through the COVID-19 pandemic added another layer of complexity to this professional caregiving experience. Although efforts have been made by employers to support health care providers in the practices of self-care and mindfulness, the true experience of care providers was not known. This study was conducted from June 2020 to October 2020 to better understand the experiences of physicians, nurses, social workers, and spiritual care providers who were providing palliative care during the first wave of the pandemic. Twenty-five participants completed the Professional Quality of Life (The Concise ProQOL Manual: The Concise Manual for the Professional Quality of Life Scale) measure and open-ended questions to evaluate their experiences as health care providers. The findings resulted in an overarching theme of "Weathering the Storm" with 6 themes describing the experiences of the interprofessional team.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.097
GPT teacher head0.449
Teacher spread0.352 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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