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Record W4387584795 · doi:10.4314/ahs.v23i2.70

Oral health delivery in refugee camps in East Region of Cameroon

2023· article· en· W4387584795 on OpenAlexaff
Kaptue Bruno, A Tetu, Ernest Tambo, Keboa Mark, Naidoo Sudeshni

Bibliographic record

VenueAfrican Health Sciences · 2023
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineRefugeeToothacheWorkforceOral healthFamily medicineOutreachHealth careOral health careEnvironmental healthNursingTraditional medicine

Abstract

fetched live from OpenAlex

Background: Oral health care affects the quality of life and plays an essential role in the general health of vulnerable populations especially refugees. The purpose of the study was to evaluate the quality of oral health care delivery in the Gado-Badzeré refugee camp in the Eastern region of Cameroon. Methods: We carried out a cross-sectional study between January and July 2020 using a structured questionnaire in French and translated orally to Fulfulde language. Results: A total of 716 refugees from the Central African Republic with ages ranging from 6 to 81 years (29.3years ± 14.6 s.d), made up of 61.2% females, 378(52.8%) unemployed, 342(47.8%) married, 701(97.9%) Muslims, and 511(71.4 %) had no formal education participated. Oral health knowledge was significantly poor, 305 (42.6%) that consulted the health post for their oral health needs were not satisfied, 640(89.50%) had experienced toothache, 592(83.0%) needed restorative treatment, 709(99.0%) periodontal treatment and 215(30.0%) urgent needs like tooth extraction. There were no oral health facilities, no oral health personnel, no oral health outreach had ever been carried out in the camp, and oral pathologies were managed by nurses with medications. Conclusion: The quality of oral health care delivery in this camp was very poor. There is an absence of oral health workforce and basic primary oral health care facilities. Oral health knowledge was very poor and the treatment needs the refugees was very high.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
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 head0.078
GPT teacher head0.375
Teacher spread0.297 · 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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