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Record W4360839090 · doi:10.1371/journal.pgph.0001618

Tuberculosis service disruptions and adaptations during the first year of the COVID-19 pandemic in the private health sector of two urban settings in Nigeria—A mixed methods study

2023· article· en· W4360839090 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePLOS Global Public Health · 2023
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 diagnosis using AI
Canadian institutionsMcGill UniversityUniversity of Waterloo
FundersMcGill UniversityBill and Melinda Gates FoundationUnited States Agency for International Development
KeywordsGovernment (linguistics)PandemicPrivate sectorHealth carePharmacyBusinessMedicineTuberculosisService providerEnvironmental healthCoronavirus disease 2019 (COVID-19)Service (business)Family medicineEconomic growthMarketingDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Nigeria has the second largest share of undiagnosed TB cases in the world and a large private health sector estimated to be the point of initial care-seeking for 67% of TB patients. There is evidence that COVID-19 restrictions disrupted private healthcare provision, but insufficient data on how private healthcare provision changed as a result of the pandemic. We conducted qualitative interviews and a survey to assess the impact of the pandemic, and government response on private healthcare provision, and the disruptions providers experienced, particularly for TB services. Using mixed methods, we targeted policymakers, and a network of clinical facilities, laboratories, community pharmacies, and medicine vendors in Kano and Lagos, Nigeria. We interviewed 11 policymakers, surveyed participants in 2,412 private facilities. Most (n = 1,676, 70%) facilities remained open during the initial lockdown period, and most (n = 1,667, 69%) offered TB screening. TB notifications dipped during the lockdown periods but quickly recovered. Clinical facilities reported disruptions in availability of medical supplies, staff, required renovations, patient volume and income. Few private providers (n = 119, 11% in Kano; n = 323, 25% in Lagos) offered any COVID-19 screening up to the time of the survey, as these were only available in designated facilities. These findings aligned with the interviews as policymakers reported a gradual return to pre-COVID services after initial disruptions and diversion of resources to the pandemic response. Our results show that COVID-19 and control measures had a temporary impact on private sector TB care. Although some facilities saw decreases in TB notifications, private facilities continued to provide care for individuals with TB who otherwise might have been unable to seek care in the public sector. Our findings highlight resilience in the private sector as they recovered fairly quickly from pandemic-related disruptions, and the important role private providers can play in supporting TB control efforts.

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)0.007
metaresearch head score (Gemma)0.003
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 score0.308
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.004
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 head0.102
GPT teacher head0.422
Teacher spread0.320 · 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