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Record W2597624671

Ethical issues experienced by healthcare workers in nursing homes: Literature review

2014· article· en· W2597624671 on OpenAlexaff
Deborah Preshaw, Kevin Brazil, Dorry McLaughlin, Andrea Frolic

Bibliographic record

VenueResearch Portal (Queen's University Belfast) · 2014
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsHamilton Health Sciences
Fundersnot available
KeywordsNursingEthical issuesBurnoutNursing ethicsHealth careWork (physics)Engineering ethicsMedicinePsychologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Background: Ethical issues are increasingly being reported by care-providers; however, little is knownabout the nature of these issues within the nursing home. Ethical issues are unavoidable in healthcareand can result in opportunities for improving work and care conditions; however, they are alsoassociated with detrimental outcomes including staff burnout and moral distress.Objectives: The purpose of this review was to identify prior research which focuses on ethical issues in thenursing home and to explore staffs’ experiences of ethical issues.Methods: Using a systematic approach based on Aveyard (2014), a literature review was conducted whichfocused on ethical and moral issues, nurses and nursing assistants, and the nursing home.Findings: The most salient themes identified in the review included clashing ethical principles, issuesrelated to communication, lack of resources and quality of care provision. The review also identifiedsolutions for overcoming the ethical issues that were identified and revealed the definitional challengesthat permeate this area of work.Conclusions: The review highlighted a need for improved ethics education for care-providers.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.621
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.436
Teacher spread0.400 · 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 designNot applicable
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

Citations8
Published2014
Admission routes1
Has abstractyes

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