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Record W3110885278 · doi:10.5430/jnep.v11n4p1

Evaluation of the effectiveness of educational technology in the prevention of falls in the surgical medical clinic

2020· article· en· W3110885278 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Nursing Education and Practice · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHealth Education and Validation
Canadian institutionsnot available
Fundersnot available
KeywordsInterquartile rangeTest (biology)MedicineFamily medicineMedical educationSurgery

Abstract

fetched live from OpenAlex

Objective: To evaluate the effectiveness of a flipchart as an educational technology for patient education on preventing falls in hospitalized patients.Methods: This is a quasi-experimental study, being a pilot study type, with a quantitative approach, carried out in a hospital in the northern region of the State of Ceará, Brazil. Thirty-one patients represented the sample from August to November 2019. For the collection of information, it has been applied a structured instrument that includes two parts: a) Clinical-epidemiological data; b) Fall prevention knowledge test.Results: In the pre-test, there was a median of correct answers of 17 (interquartile range = 8), while in the post-test the median of correct answers was 20 (interquartile range = 6). Significance was observed in the increase in average of theoretical correct answers (p < .000).Conclusions: The flipchart is an effective technology to be used in health education to prevent falls, favoring an increased perception of risks in the hospital environment.

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.

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)0.032
metaresearch head score (Gemma)0.031
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0320.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.286
GPT teacher head0.607
Teacher spread0.321 · 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