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Record W7100186749

Quasiexperimental Design; *Skill Development IDENTIFIERS Nursing Students

2016· article· en· W7100186749 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsnot available
Fundersnot available
KeywordsTask (project management)Quarter (Canadian coin)Scale (ratio)Health careDreyfus model of skill acquisitionTask analysis
DOInot available

Abstract

fetched live from OpenAlex

Adaptive competencies are the skills required to effectively complete a particular task and are the congruencies (balance) between personal skills and task demands. The differences between the adaptive competency acquisition of students in licensed practical nurse (LPN) programs and associate degree nurse (ADN) programs were examined in a quasiexperimental study. A 34-item profile and a 3-question demographic assessment were administered to 30 students enrolled in the final quarter of an LPN program and 41 students enrolled in the beginning quarter of an ADN program. Individual scores and group means for the individual Likert-type scale responses were determined. No significant differences were found in how the students enrolled in the LPN program and students enrolled in the ADN program developed the following types of competencies: (1) accommodative competencies; (2) assimilative competencies; (3) convergent competencies; and (4) divergent competencies. Gender, age, and previous health care experiences

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)0.031
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.040
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.002
Science and technology studies0.0050.008
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0230.004

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 head0.033
GPT teacher head0.359
Teacher spread0.326 · 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 designNon-randomized trial
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

Citations0
Published2016
Admission routes1
Has abstractyes

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