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Record W2156653513 · doi:10.1258/135763305775124911

An e-health needs assessment of medical residents in Cameroon

2005· article· en· W2156653513 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2005
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTelemedicinePhoneMobile phoneMedicineNeeds assessmentRural areaHealth careIsolation (microbiology)Medical emergencyNursingFamily medicineComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Medical residents from Yaounde I University in Cameroon are required to spend periods of time in rural or remote locations to complete their training. To determine if e-health might lessen their isolation and enhance patient care, a needs assessment of the residents was performed using a brief questionnaire (five items) about the situation in which residents found themselves outside their medical school environment. We gave the questionnaires to 45 residents. Seventeen questionnaires had been returned at the time of the site visit, a response rate of 38%. Most residents indicated that the ability to contact a mentor would have either made them feel more confident (16, or 94%) or altered their handling of recent cases (15, or 88%). All residents had access to a mobile phone, and many (11, or 65%) had used it to contact a medical colleague for guidance. A low-cost and technologically simple telemedicine solution that maximized use of mobile phone capability, provided access to medical and health-care information, and permitted exchange of images would be an appropriate response to the identified needs.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.359
Teacher spread0.338 · 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