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Record W2063998645 · doi:10.1191/0269216302pm585oa

Clinical nurse specialists in palliative care. Part 2. Explaining diversity in the organization and costs of Macmillan nursing services

2002· article· en· W2063998645 on OpenAlexaff
David Clark, Jane Seymour, Hannah-Rose Douglas, Peter Bath, Nicola Beech, Jessica Corner, Deborah Halliday, Philippa Hughes, Joanne Haviland, Charles Normand, Rachael Marples, Julie Skilbeck

Bibliographic record

VenuePalliative Medicine · 2002
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsInstitute of Cancer Research
Fundersnot available
KeywordsNursingContext (archaeology)TeamworkPalliative careDiversity (politics)Service (business)MedicineWork (physics)Resource (disambiguation)SociologyBusinessManagementMarketing

Abstract

fetched live from OpenAlex

In the UK, the work of Macmillan clinical nurse specialists in palliative care is now well established. There has been little research, however, into the organizational context in which these nurses operate and the implications for the services they deliver. We report on a major evaluation of the service delivery, costs, and outcomes of Macmillan nursing services in hospital and community settings. The study was based on eight weeks of fieldwork in each of 12 selected services. Data are presented from semi-structured interviews, clinical records, and cost analysis. We demonstrate wide variation across several dimensions: location and context of the services; activity levels; management patterns; work organization and content; links with other colleagues; and resource use. We suggest that such variation is likely to indicate the existence of both excellent practice and suboptimal practice. In particular, our study highlights problems in how teamwork is conceptualized and delivered. We draw on recent organizational theories to make sense of the heterogeneous nature of Macmillan nursing services.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.104
GPT teacher head0.475
Teacher spread0.371 · 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

Citations39
Published2002
Admission routes1
Has abstractyes

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