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Record W4214578359 · doi:10.1177/10547738221078901

Perceived Therapeutic Self-Care Ability of Patients in Surgical Units: A Multisite Survey

2022· article· en· W4214578359 on OpenAlexaff
Hussan Zeb, Ahtisham Younas, Amara Sundus, Mubashir Iqbal, Khurram Ishaq

Bibliographic record

VenueClinical Nursing Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineSelf carePsychological interventionAffect (linguistics)Nursing careFamily medicineNursingPhysical therapyPsychologyHealth care

Abstract

fetched live from OpenAlex

Assessing patients’ therapeutic self-care ability allows nurses to initialize care and implement interventions to enhance their self-care abilities. However, sociocultural beliefs and determinants can affect patients’ self-care practices. This study determined perceived therapeutic self-care ability of patients in surgical units in Pakistan. A survey was conducted using a purposive sample 511 patients admitted to surgical units for at least 24 hours. Data were collected using the Urdu version of Therapeutic Self-Care Measure. The mean self-care ability score was 20.05 ± 4.3. Patients felt more prepared to take their medications, but less prepared to respond to any unforeseeable physical changes. Significant difference was found between self-care ability of male (20.68 ± 4.15) and female (19.18 ± 4.27) ( p < .001) patients. A weak negative correlation was found between patient age and self-care ability ( r = −0.15, p = .001). Self-care ability assessment should be included in routine discharge planning, and nurses should provide more tailored self-care discharge education to surgical patients.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.379
GPT teacher head0.576
Teacher spread0.197 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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