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Record W2606056119 · doi:10.4102/hsag.v23i0.1083

Experiences of partners of professional nurses venting traumatic information

2018· article· en· W2606056119 on OpenAlexaff
Tinda Rabie, Melanie Wehner, Magdalena P. Koen

Bibliographic record

VenueHealth SA Gesondheid · 2018
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsHealth Sciences North
Fundersnot available
KeywordsStressorNonprobability samplingCoping (psychology)PsychologyNursingReciprocalQualitative researchCoding (social sciences)Content analysisMedicineClinical psychologyPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: Professional nurses employed in trauma units encounter numerous stressors in their practice environment. They use different strategies to cope with this stress, including venting traumatic information to their partners and other family members. AIMS: To describe how partners of professional nurses cope with traumatic information being vented to them. METHODS: A qualitative research method with an interpretive descriptive inquiry design was used to explore, interpret and describe the coping experiences of the nurses' partners. Purposive sampling was used to select a total of 14 partners, but only ten participated in semi-structured interviews. Tesch's eight steps of open coding were used for data analysis. RESULTS: Four main themes were identified indicating adaptive and maladaptive coping skills, namely partners' experiences of traumatic information vented to them; partners' coping activities; reciprocal communication and relationship support between partners and nurses; and resilience of partners to deal with the nursing profession. CONCLUSION: Partners employed different ways to cope with traumatic information. It was essential for partners and nurses to be supported by nurses' practice environments and to develop resilience to fulfil reciprocal supportive roles in their relationships.

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)0.001
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 score0.074
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.125
GPT teacher head0.478
Teacher spread0.353 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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