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Record W2810797144 · doi:10.5430/jnep.v8n11p47

Pediatric vision screening: The Colorado school nurse experience

2018· article· en· W2810797144 on OpenAlexvenueno aff
Rebecca Braverman

Bibliographic record

VenueJournal of Nursing Education and Practice · 2018
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Health Research
Canadian institutionsnot available
Fundersnot available
KeywordsNursingOptometryMedicineScreening testPerspective (graphical)Medical screeningSchool nursePsychologyMedical educationFamily medicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Colorado schools are required by law to perform vision screening. The aim of the study was to elucidate the vision screening process from the school nurse perspective. An electronic questionnaire distributed to Colorado school nurses, and developed by the author, was used to query Colorado nurses about the logistical challenges they face when performing vision screening. Common challenges faced when performing vision screening included: inadequate staff to perform vision screening, inadequate space to complete testing, uncooperative students, and poor parental compliance with referrals for further evaluation after their child failed their vision screen. Most nurses do not use instrument-based screening. Many nurses did not properly occlude the eyes when performing monocular visual acuity testing, which may lead to false negative screening results. The results of the questionnaire suggest there are opportunities to improve the vision screening process by nurses in Colorado schools.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.250
GPT teacher head0.626
Teacher spread0.376 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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