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Record W2974522007 · doi:10.1177/0844562119870419

Obstetrical Nurses’ Perspectives of Pregnant Women Who Use Illicit Substances and Their Provision of Care: A Thematic Analysis

2019· article· en· W2974522007 on OpenAlexafffundvenueabout
Jenna Menard-Kocik, Vera Caine

Bibliographic record

VenueCanadian Journal of Nursing Research · 2019
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsUniversity of AlbertaAlberta Health Services
FundersCanadian Institutes of Health Research
KeywordsThematic analysisContent analysisQualitative researchNursingDistressPsychologyMedicineSociologyClinical psychology

Abstract

fetched live from OpenAlex

BACKGROUND: The growing number of pregnant women using illicit substances presents a serious public health concern. PURPOSE: The purpose of this study was to explore obstetrical nurses' perspectives toward caring for pregnant women who use illicit substances in a large inner-city hospital in Western Canada. METHODS: Guided by an interpretivist and social constructivist epistemological approach, I engaged in a thematic content analysis of qualitative semistructured interviews. In total 18 registered nurses from multiple obstetrical units were recruited. RESULTS: Four major themes were identified: (i) services and care were recognized through elements of caring, creating a welcoming environment, and providing client-centered care; (ii) stigma and discrimination impacted nurses preconceptions of care; (iii) coping mechanisms were necessary when struggling professionally; and (iv) recommendations of continuing specialized education were identified. CONCLUSION: Obstetrical nurses highlighted a number of conflicting views about caring for pregnant women who use illicit substances. Key actions, such as establishing professional support when nurses experience ethical distress or when they are unable to provide meaningful care to patients, were suggested. Strong recommendations for ongoing professional development, as well as increased educational opportunities during prelicensure programs were made in order to support nurses in their role.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.138
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
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 head0.033
GPT teacher head0.340
Teacher spread0.307 · 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 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

Citations8
Published2019
Admission routes4
Has abstractyes

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