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Record W3004359986 · doi:10.26443/ijwpc.v7i1.226

Team Well-being and Resilience Practices in Hospice and Palliative Care

2020· article· en· W3004359986 on OpenAlexvenueno aff
Glen Komatsu

Bibliographic record

VenueInternational Journal of Whole Person Care · 2020
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsJournaling file systemActive listeningBurnoutPsychologyEmotional intelligenceNursingCompassion fatiguePsychological resilienceEmpathyTeamworkCompassionPalliative careProcess (computing)Medical educationMedicinePsychotherapistSocial psychologyClinical psychology

Abstract

fetched live from OpenAlex

High functioning, resilient teams do not happen by chance. Teams, similar to individuals, need to be educated, nurtured and formed over time, by a consistent vision and process. With proper team formation, the compassionate care of patients, families and colleagues can be developed, modeled and reinforced. Self-compassion is another focus to help caregivers cope with the stresses of the work and mitigate against burnout. The primary intervention which will be discussed is a regularly scheduled reflection process, e.g. 30 minutes weekly or 90 minutes monthly, with a pediatric hospice team, an inpatient palliative care team and an outpatient palliative care team. The reflection process incorporates mindful meditation, journaling, listening exercises, individual and group reflection to encourage and practice self-awareness, self-reflection, greater emotional intelligence and leadership skills. Specific tools employed include the Search Inside Yourself © Program, books by various authors, selected music, videos and personal journals. Qualitative feedback from team members, patient, family and colleague satisfaction scores has been positive. Buy-in from all team members, initially, was difficult, but over time, all team members have recognized the value of the process and have incorporated the exercises not only in their work, but in their personal lives and other roles/jobs. Other key success factors are organizational support for time for this process and individual champions to develop and lead the reflective process. The workshop will include a demonstration of exercises used in team reflections with learner participation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.082
GPT teacher head0.421
Teacher spread0.339 · 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 designObservational
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

Citations1
Published2020
Admission routes1
Has abstractyes

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