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Professional wellbeing and caring: exploring a complex relationship

2013· article· en· W91737066 on OpenAlexaff
Kate Beckett

Bibliographic record

VenueBritish Journal of Nursing · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsImpact
Fundersnot available
KeywordsNursingPsychologyEmpathyMedicineSocial psychology

Abstract

fetched live from OpenAlex

There is growing concern about lack of compassion in nursing. Impact of Injuries, which is the parent study (Kendrick et al, 2011) of this independent nested study, collected patient accounts of care received by physiotherapists and nurses. While physiotherapists were generally described as caring, nursing care was less consistent and sometimes uncaring. This embedded study conducted semi-structured interviews in 2012 with 11 physiotherapists and 12 nurses in four English hospitals to obtain perspectives on the provision of care. Physiotherapists presented a distinct identity with caring both integral to the role and sustained by structural and organisational factors. Nurses had a diffuse identity with limited control within a medical and business model of care. They appeared 'under siege' and were nostalgic for caring, which was frequently subordinate to other demands. Both nurses and physiotherapists faced challenges but nurses felt the context of their work was not conducive to caring. This article draws comparisons between these professions and makes informed recommendations to improve nursing practice and patient care.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0080.015
Scholarly communication0.0110.010
Open science0.0010.015
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.239
GPT teacher head0.445
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2013
Admission routes1
Has abstractyes

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