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Record W4285591622 · doi:10.1177/08258597221105001

Factors That Determine the Experience of Transition to an Inpatient Palliative Care Unit for Patients and Caregivers: A Qualitative Study

2022· article· en· W4285591622 on OpenAlexaffabout
Katherine Whitehead, Kari Ala‐Leppilampi, Betty Lee, Jacqueline Menagh, Donna Spaner

Bibliographic record

VenueJournal of Palliative Care · 2022
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPalliative careUnit (ring theory)Qualitative researchMedicineNursingFamily medicinePsychologySociology

Abstract

fetched live from OpenAlex

Objective: Transitions in care settings near the end of life can present challenges to patients and families, especially when there are also adjustments in level of care and illness trajectory. In this study, we explored what factors influenced how patients and family caregivers experienced a transition to an inpatient Palliative Care Unit (PCU). Methods: This qualitative study was conducted at a PCU in Toronto, Canada. Semi-structured interviews were held with 29 participants (14 patients and 15 family caregivers) during their time on the PCU. Data was analyzed through an iterative process of constant comparison to generate themes. The recruitment process continued to the point of thematic saturation. Results: Five themes were identified that represented the participants’ experiences in transitioning to the PCU: Being prepared, Feeling supported, Coming to terms with end of life issues, Dealing with uncertainty, and Continuity of care. Conclusions: Our findings highlight the need for clear and iterative communication with patients and family caregivers during the transition to a PCU. Identification and consideration of the common themes involved in the experience of transfer to PCU can help guide future practice and improve the experience of patients and families during transitions at the end of life.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.252
GPT teacher head0.474
Teacher spread0.222 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

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