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Record W4389201656 · doi:10.1186/s12913-023-10317-w

Beyond the regulatory radar: knowledge and practices of rural medical practitioners in Bangladesh

2023· article· en· W4389201656 on OpenAlexaff
Hasnat Sujon, Mohammad Habibur Rahman Sarker, Aftab Uddin, Shakila Banu, Mohammod Rafiqul Islam, Md Ruhul Amin, Md. Shabab Hossain, Md. Fazle Alahi, Mohammad Asaduzzaman, Syed Jafar Raza Rizvi, Mohammad Zahirul Islam, Nazim Uzzaman

Bibliographic record

VenueBMC Health Services Research · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Saskatchewan
FundersInternational Centre for Diarrhoeal Disease Research, BangladeshMedizinischen Hochschule Hannover
KeywordsMedicinePublic healthHealth administrationGuidelinePneumoniaFamily medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Informal and unregulated rural medical practitioners (RMPs) provide healthcare services to about two-thirds of people in Bangladesh, although their service is assumed to be substandard by qualified providers. As the RMPs are embedded in the local community and provide low-cost services, their practice pattern demands investigation to identify the shortfalls and design effective strategies to ameliorate the service. METHODS: We conducted a cross-sectional study in 2015-16 using a convenient sample from all 64 districts of Bangladesh. Personnel practising modern medicine, without any recognized training, or with recognized training but practising outside their defined roles, and without any regulatory oversight were invited to take part in the study. Appropriateness of the diagnosis and the rationality of antibiotic and other drug use were measured as per the Integrated Management of Childhood Illness guideline. RESULTS: We invited 1004 RMPs, of whom 877 consented. Among them, 656 (74.8%) RMPs owned a drugstore, 706 (78.2%) had formal education below higher secondary level, and 844 (96.2%) had informal training outside regulatory oversight during or after induction into the profession. The most common diseases encountered by them were common cold, pneumonia, and diarrhoea. 583 (66.5%) RMPs did not dispense any antibiotic for common cold symptoms. 59 (6.7%) and 64 (7.3%) of them could identify all main symptoms of pneumonia and diarrhoea, respectively. In pneumonia, 28 (3.2%) RMPs dispensed amoxicillin as first-line treatment, 819 (93.4%) dispensed different antibiotics including ceftriaxone, 721 (82.2%) dispensed salbutamol, and 278 (31.7%) dispensed steroid. In diarrhoea, 824 (94.0%) RMPs dispensed antibiotic, 937 (95.4%) dispensed ORS, 709 (80.8%) dispensed antiprotozoal, and 15 (1.7%) refrained from dispensing antibiotic and antiprotozoal together. CONCLUSIONS: Inappropriate diagnoses, irrational use of antibiotics and other drugs, and polypharmacy were observed in the practising pattern of RMPs. The government and other stakeholders should acknowledge them as crucial partners in the healthcare sector and consider ways to incorporate them into curative and preventive care.

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)0.023
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.100
GPT teacher head0.550
Teacher spread0.449 · 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.

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

Citations9
Published2023
Admission routes1
Has abstractyes

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