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Record W2167695248 · doi:10.1177/1074840706290806

Development of the Family Nursing Practice Scale

2006· article· en· W2167695248 on OpenAlexaff
Peggy Simpson, Marie Tarrant

Bibliographic record

VenueJournal of Family Nursing · 2006
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCronbach's alphaPsychologyConstruct validityScale (ratio)Competence (human resources)Reliability (semiconductor)NursingNursing practiceCritical appraisalContent validityApplied psychologyPsychometricsClinical psychologyMedicineSocial psychologyAlternative medicine

Abstract

fetched live from OpenAlex

This article describes the development and testing of the Family Nursing Practice Scale (FNPS). This self-report questionnaire is designed to measure perceived changes in family nursing practice including attitudes toward working with families, critical appraisal of their family nursing practice and reciprocity in the nurse-family relationship. Categories were derived from a needs assessment, competence as effective application of knowledge and skill and theoretical foundations for family assessment and intervention. Psychometric testing (content, construct validity, internal consistency, and test-retest reliability) was undertaken with 140 psychiatric nurses in Hong Kong. Practice appraisal and nurse-family relationships accounted for 56.4% of the variance. Cronbach's alpha reliability coefficients were .88 and .73 for the two subscales, respectively, and .86 for the scale overall. Test-retest reliability ranged from .62 to .93 on the individual items. The results provide preliminary evidence of the reliability and validity of the FNPS. The instrument provides quantitative and qualitative evaluation components.

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)0.007
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.096
GPT teacher head0.419
Teacher spread0.323 · 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 designBench or experimental
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

Citations61
Published2006
Admission routes1
Has abstractyes

Explore more

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