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Lamentation and loss: expressions of caring by contemporary surgical nurses

2007· review· en· W1987352836 on OpenAlexaffabout
Carol Enns, David Gregory

Bibliographic record

VenueJournal of Advanced Nursing · 2007
Typereview
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTheme (computing)GriefNursingSadnessAttunementPerspective (graphical)PsychologyLamentMedicinePsychotherapistSocial psychologyAlternative medicine

Abstract

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AIM: This paper is a report of a phenomenological study of caring from the perspective of nurses working on surgical wards. BACKGROUND: While care and caring are complex foundational nursing concepts which have received considerable and ongoing attention from theorists, researchers and clinicians, there has been little research into caring on surgical units. METHOD: A convenience sample of ten nurses working on surgical units in a public teaching hospital in Canada was interviewed using van Manen's phenomenological approach. Data were collected during 2001 using semi-structured interviews. FINDINGS: The major theme of lamentation and loss was identified from the data. Participants revealed a dichotomous tension between what caring should be and what actually occurs. This tension was pervasive and generated lament - an expression of grief and mourning for the loss of caring. The essential structures supporting this theme included lack of time, lack of caring support, tasking, increased acuity, lack of continuity of care, emotional divestment and not caring for each other. Loss and sadness were articulated and participants lamented and grieved about the loss of care in contemporary practice. CONCLUSION: The forces and influences described by participants undermined caring in the new practice milieu. If this is a glimpse of the future, then the values of the nursing profession may be under siege. Caring as the central core, the essence or unifying concept of nursing may be subject to marginalization in contemporary practice.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.061
GPT teacher head0.436
Teacher spread0.375 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations28
Published2007
Admission routes2
Has abstractyes

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