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Record W1607204341 · doi:10.1111/iwj.12279

The lived experience of the wound care nurse in caring for patients with pressure ulcers

2014· article· en· W1607204341 on OpenAlexaff
Marlene Varga, Samantha Holloway

Bibliographic record

VenueInternational Wound Journal · 2014
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsCovenant Health
FundersCardiff University
KeywordsMedicineLived experienceTemporalityHermeneutic phenomenologyCoping (psychology)Phenomenology (philosophy)NursingHealth careQualitative researchPsychologyPsychotherapistClinical psychologySociology

Abstract

fetched live from OpenAlex

The aim of the study was to report the lived experience of the wound care nurse (WCN) in caring for patients with pressure ulcers (PU). WCN play an important role in caring for patients with PU, but the effect on caring for individuals with such wounds is poorly understood. A descriptive and interpretative study on the life worlds of spatiality, temporality, relationality and corporeality was carried out. Utilising the hermeneutic Heideggerian phenomenology, data were collected over a 3-month period in 2012 using in-depth interviews with five WCN. The interviews revealed eight themes: 'challenge', 'making sense of it all', 'coping and self-care', 'advocate of mine/making a difference', 'knowledge and technology', 'we have seen what can happen', 'holistic caring' and 'frustration'. Twenty-five sub-themes were also identified. WCN experienced a demanding and rewarding role of caring, influenced by the environment and the challenges with individuals living with PU. This study demonstrated an enriching yet challenging role. Recommendations for WCN, health care authorities and education providers include raising awareness of the importance of self-care, greater recognition of the effect of this role on patients with PU and changing education to include reflective practice and resilience strategies.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.023
GPT teacher head0.365
Teacher spread0.342 · 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 designObservational
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
Published2014
Admission routes1
Has abstractyes

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