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Record W2330969225 · doi:10.1097/njh.0000000000000138

When Patients Mirror Our Personal Lives

2015· article· en· W2330969225 on OpenAlexaff
Maria Chiera-Lyle, Rabbi Rena Arshinoff

Bibliographic record

VenueJournal of Hospice and Palliative Nursing · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsToronto Western HospitalUniversity Health Network
Fundersnot available
KeywordsWitnessPsychologyFace (sociological concept)NursingWork (physics)Personal developmentPersonal lifePersonal careMedicinePsychotherapistFamily medicineSociology

Abstract

fetched live from OpenAlex

Nurses bring professional knowledge and personal experience to their work. Sometimes they relate to patients because of similar situations they have experienced themselves. One of the challenges nurses face is separating out the situation of their patients from their own incidents that seem very familiar. Although nurses are trained to keep their own emotional responses detached from the work they do, they sometimes unconsciously identify with the emotions they witness in their patients and families. When situations our patients face mirror our personal lives, nurses and professional caregivers may not recognize the impact they have on them. The spiritual care professional supports staff with the awareness that nurses are also human themselves with their own personal challenges and accompanying emotions. There is great potential for nurses to obtain spiritual support as they strive to provide optimum care to patients as they deal with their own challenges. This article highlights the experience of a nurse and a spiritual care professional who helped her to identify the parallel between her patient’s situation and that of her own and the spiritual and emotional growth that emerged on both a personal and a professional level.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.093
GPT teacher head0.407
Teacher spread0.314 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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