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Record W4229024002 · doi:10.3928/00220124-20220414-01

Developing and Pilot Testing E-Learning Training for Pediatric Nursing Burn Care

2022· article· en· W4229024002 on OpenAlexaff
Julie Farthing, Sylvie Le May, Jérôme Gauvin‐Lepage

Bibliographic record

VenueThe Journal of Continuing Education in Nursing · 2022
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsPediatric burnMedicineIntervention (counseling)NursingPediatric nursingNursing careMEDLINETest (biology)

Abstract

fetched live from OpenAlex

Background Nurses caring for pediatric patients who have burns need to be properly trained to provide optimal care. The aims of this pilot study were to (1) develop a pediatric nursing burn care e-learning training for novice nurses; (2) assess the feasibility and acceptability of this educational intervention; and (3) evaluate the preliminary effects of this intervention on novice nurses' knowledge of pediatric nursing burn care. Method A quasi-experimental, one-group, pre-test–posttest design was used. Results Feasibility was achieved because all of the participants completed the study. A significant difference was observed in the mean knowledge level of the novice nurses from before training to after training (87.7% ± 8.7% vs. 58.6% ± 14.5%; p < .001). The novice nurses' had a mean satisfaction of 95.5% after the intervention. Conclusion This new, evidence-based pediatric nursing burn care e-learning training appeared to be feasible. The novice nurses found it to be satisfactory, and it improved their knowledge regarding pediatric burn care. [ J Contin Educ Nurs . 2022;53(5):232–240.]

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.355
Teacher spread0.313 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations2
Published2022
Admission routes1
Has abstractyes

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