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Record W2050581797 · doi:10.1002/nur.20110

Relationship between nursing interventions and outcome achievement in acute care settings

2006· article· en· W2050581797 on OpenAlexaff
Diane Doran, Margaret B. Harrison, Heather Spence Laschinger, John P. Hirdes, Ellen Rukholm, Souraya Sidani, Linda M. Hall, Ann E. Tourangeau, Lisa Cranley

Bibliographic record

VenueResearch in Nursing & Health · 2006
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsInstitute for Clinical Evaluative SciencesMinistry of Health and Long Term CareResearch CanadaLaurentian UniversityUniversity of TorontoHomewood Research InstituteWestern UniversityQueen's UniversityCanadian Institutes of Health ResearchUniversity of Waterloo
Fundersnot available
KeywordsPsychological interventionMedicineMinimum Data SetNursing Interventions ClassificationAuditNursingAcute careNursing careDocumentationHealth careMEDLINEScale (ratio)Physical therapyNursing homes

Abstract

fetched live from OpenAlex

The extent to which nursing interventions provided during hospitalization are associated with patients' therapeutic self-care and functional health outcomes was explored with a voluntary sample of 574 patients. Nurses collected data on patient outcomes at admission and discharge using the minimum data set (MDS) and the therapeutic self-care scale (TSCS). Research assistants audited charts for documentation of nursing interventions. The results indicated that nursing interventions aimed at exercise promotion, positioning, and self-care assistance predicted functional status outcome. Higher functional status outcome predicted therapeutic self-care ability at hospital discharge. The results demonstrate that nurses can use MDS and TSCS data on patient outcomes to gain insight into the effectiveness of their interventions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.235
GPT teacher head0.584
Teacher spread0.350 · 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 source (direct Gemma or distilled Codex), 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

Citations76
Published2006
Admission routes1
Has abstractyes

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