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Record W2165907622 · doi:10.1177/0193945910373600

A Questionnaire for Assessing Community Health Nurses’ Learning Needs

2010· article· en· W2165907622 on OpenAlexaffabout
Noori Akhtar‐Danesh, Ruta Valaitis, Ruth Schofield, Jane Underwood, Ruth Martin‐Misener, Andrea Baumann, Camille Kolotylo

Bibliographic record

VenueWestern Journal of Nursing Research · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsDalhousie UniversityMcMaster University
Fundersnot available
KeywordsNeeds assessmentPsychologyMedical educationExploratory factor analysisReliability (semiconductor)Confirmatory factor analysisNursingMedicinePsychometricsComputer scienceClinical psychology

Abstract

fetched live from OpenAlex

Learning needs assessment is an important stage of every educational process that aims to inform changes in practice and policy for continuing professional development. Professional competencies have been widely used as a basis for the development of learning needs assessment. The Canadian Community Health Nursing Standards of Practices (CCHN Standards) were released in 2003. However, it is not known whether community health nurses (CHNs) have the educational background to enable them to meet these standards. This article reports on the development of a learning needs assessment questionnaire for CHNs. Exploratory and confirmatory factor analyses were conducted to examine the consistency of factors underpinning the CCHN Standards. Also, validity and reliability of the questionnaire were evaluated using appropriate techniques. This process resulted in a valid and reliable CHN learning needs assessment questionnaire to measure learning needs of large groups of practitioners, where other forms of measurement cannot be feasibly conducted.

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)0.058
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0580.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.513
GPT teacher head0.673
Teacher spread0.161 · 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 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

Citations19
Published2010
Admission routes2
Has abstractyes

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