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Record W2899723015 · doi:10.1111/nin.12269

Invisibility of the self: Reaching for the <i>telos</i> of nursing within a context of moral distress

2018· article· en· W2899723015 on OpenAlexaff
Carolina da Silva Caram, Elizabeth Peter, Maria José Menezes Brito

Bibliographic record

VenueNursing Inquiry · 2018
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Toronto
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsTelosInvisibilityThematic analysisNursing ethicsNursingContext (archaeology)Virtue ethicsQualitative researchDistressPsychologySociologyVirtueSocial psychologyMedicineEpistemologyPsychotherapistPsychiatrySocial sciencePhilosophy

Abstract

fetched live from OpenAlex

Many studies have examined clinical and institutional moral problems in the practice of nurses that have led to the experience of moral distress. The causes and implications of moral distress in nurses, however, have not been understood in terms of their implications from the perspective of virtue ethics. This paper analyzes how nurses reach for the telos of their practice, within a context of moral distress. A qualitative case study was carried out in a private hospital in Brazil. Observation and semi-semistructured interviews were conducted with 13 nurse participants. With the aid of ATLAS.ti software, the data were analyzed by using thematic content analysis using virtue ethics to theorize the findings. These nurses experienced a loss of their nursing identity as they encountered an ambiguous telos and the domination of institutional values. In their reach for the telos of their practice, nurses found an environment permeated by ethical challenges, which not only created moral distress but also created professional invisibility, a phenomenon referred to as 'invisibility of the self'.

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)0.008
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.244
GPT teacher head0.519
Teacher spread0.275 · 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 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

Citations19
Published2018
Admission routes1
Has abstractyes

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