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Record W4385716815 · doi:10.1177/10598405231193361

Knowing What We Don’t Know: A Survey of School Nurse Information Needs

2023· article· en· W4385716815 on OpenAlexaff
Annie Nickum, Rosie Hanneke, Robin Adair Shannon

Bibliographic record

VenueThe Journal of School Nursing · 2023
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsInstitute of Population and Public Health
Fundersnot available
KeywordsWorkforceInformation needsNeed to knowWork (physics)Needs assessmentNursingMedical educationPsychologySchool nurseMedicineComputer scienceSociology

Abstract

fetched live from OpenAlex

Providing complex care for students requires an informed school nurse workforce. However, school nurses, many of whom work independently, may not know where to find necessary information. This study seeks to understand the information needs of school nurses by conducting a needs assessment survey within the state of Illinois. A survey was disseminated through three statewide professional listservs to determine the types of care-related questions school nurses ask as part of their regular duties and which resources they use to answer those questions. School nurses' information needs vary widely, and they rely on numerous sources to answer clinical questions. They are responsible for the well-being of hundreds to thousands of children. While they are comfortable searching for information, they are motivated to further develop research skills.

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)0.009
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.101
GPT teacher head0.450
Teacher spread0.350 · 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 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

Citations3
Published2023
Admission routes1
Has abstractyes

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