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Record W3010945049 · doi:10.22605/rrh5109

State of the eye health system in the Pacific: is medical technology available and used by mid-level eye care workers?

2020· article· en· W3010945049 on OpenAlexaff
Benoît Tousignant, Matthew Pearce, Julie Brûlé, Biu Sikivou, Graeme Nicholls

Bibliographic record

VenueRural and Remote Health · 2020
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsUniversité de MontréalAssociation of Canadian College and University Teachers of English
Fundersnot available
KeywordsMedicineEye careOptometryWorkforceHealth careRefractive errorMedical emergencyEye diseaseOphthalmology

Abstract

fetched live from OpenAlex

INTRODUCTION: The aim of this study is to describe the availability, use and comfort with ophthalmic equipment and medications by mid-level eye care workers in Papua New Guinea and Pacific Island countries and territories as indicators of the state of eye care in the Pacific. METHODS: Health information system data, from a workforce support program to Pacific mid-level eye care workers, were analysed for availability and comfort with use of ophthalmic equipment and topical medications. RESULTS: For refraction equipment, access was excellent (98% for retinoscopes and trial lenses) 'very frequent use' range was 42-74% and 'high comfort of use' range was 54-86%. Equipment for ocular health assessment is widely available (slit lamps 67%), with high comfort levels (78-100% 'very comfortable'). Over 70% of respondents have access to topical diagnostic medications, 98% have access to at least one type of antibiotic drops and 63% have access to at least one topical corticosteroid. CONCLUSION: Overall, trained mid-level eye care workers in the Pacific seem well equipped for ocular health and refractive assessments. Comfort levels are encouraging, but also highlight areas for continuing professional development. Access to ophthalmic medications appears acceptable in the region for low morbidity anterior segment conditions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.235
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.037
GPT teacher head0.343
Teacher spread0.306 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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