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Non healing leg ulcers and the nurse–patient relationship. Part 2: the nurse’s perspective

2008· article· en· W2158293040 on OpenAlexaff
Philip A Morgan, Christine Moffatt

Bibliographic record

VenueInternational Wound Journal · 2008
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsThames Valley Children's Centre
Fundersnot available
KeywordsMedicineConcordanceNursingDistancingDistressGeneral partnershipPerspective (graphical)Community nursingAnxietyPsychiatryClinical psychologyCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

This paper focuses on the experiences of four community nursing teams responsible for the care of a small group of patients with leg ulcers who they had identified as 'non healing' and 'non concordant' with treatment. Four focus groups were held, one with each community nursing team, to examine the issues underpinning the labelling of these patients as 'non healing' and 'non concordant'. There was an expectation that patients should obey treatment instructions and be positive and participative and there was a strongly felt link between concordance and healing of the ulcer. However, limited non concordance was considered to be acceptable as long as the patient continued to progress. Nurses viewed ulcer healing as the priority even though this was unlikely and differed from the patient's priority of achieving comfort. Patient behaviour was an important determinant of labelling by nurses. Efforts by patients to exert some control over their own care were met with them being viewed as 'difficult', 'uncooperative' and 'non compliant'. There was also a pervasive level of stress, distress and anxiety among the community nurse participants, which led to distancing and blaming that undermined the nurse-patient relationship. At the centre of a successful nurse-patient relationship is a non judgemental partnership that can often be challenging to achieve especially when ulcers fail to heal.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.064
GPT teacher head0.434
Teacher spread0.370 · 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

Citations33
Published2008
Admission routes1
Has abstractyes

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