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Record W2598793288 · doi:10.2147/amep.s128403

Writing skills enhancement for public health professionals in Rwanda

2017· article· en· W2598793288 on OpenAlexaffabout
Raywat Deonandan, Nodine Sangwa, Steve Kanters, Sabin Nsanzimana

Bibliographic record

VenueAdvances in Medical Education and Practice · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of British ColumbiaUniversity of Ottawa
Fundersnot available
KeywordsPublishingMedical educationPsychological interventionPublic healthPeer reviewPsychologyJournal of Public HealthPublic health interventionsMedicinePolitical scienceNursingHealth educationInternational health

Abstract

fetched live from OpenAlex

In 2013, Canadian scholars delivered a 1-week workshop to 30 junior public health professionals in Rwanda. The goal was to improve the Rwandans' skills and confidence with respect to writing scientific papers for submission to international peer-reviewed global health journals. As a result of the workshop, there was a statistically significant improvement in participants' reported confidence in many aspects of navigating the publishing process, but no improvement in confidence regarding statistically analyzing their data. Remarkably, as a group, participants were able to write an article for a leading international journal, which was subsequently published. Results indicate that similar interventions would be both successful and well received, especially if targeted to individuals at a similar stage of career progress.

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)0.008
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.001
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.042
GPT teacher head0.515
Teacher spread0.473 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations5
Published2017
Admission routes2
Has abstractyes

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