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Record W3161728471 · doi:10.1186/s12879-021-06136-1

Acceptability, feasibility, and impact of a pilot tuberculosis literacy and treatment counselling intervention: a mixed methods study

2021· article· en· W3161728471 on OpenAlexafffund
Stephanie Law, Boitumelo Seepamore, Olivia Oxlade, Nondumiso Sikhakhane, Halima Dawood, Sheldon Chetty, Nesri Padayatchi, Dick Menzies, Amrita Daftary

Bibliographic record

VenueBMC Infectious Diseases · 2021
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill UniversityCentre for Global Health ResearchMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsMedicineTuberculosisFamily medicineMotivational interviewingIntervention (counseling)Poisson regressionHealth literacyTest (biology)Latent tuberculosisTuberculosis diagnosisHealth careNursingPopulationMycobacterium tuberculosisEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: There is a need for innovative strategies to improve TB testing uptake and patient retention along the continuum of TB care early-on in treatment without burdening under-resourced health systems. We used a mixed methods approach to develop and pilot test a tuberculosis literacy and counselling intervention at an urban clinic in KwaZulu Natal, South Africa, to improve TB testing uptake and retention in tuberculosis care. METHODS: We engaged in discussions with clinic staff to plan and develop the intervention, which was delivered by senior social work students who received one-week training. The intervention included: 1) group health talks with all patients attending the primary clinic; and 2) individual counselling sessions, using motivational interviewing techniques, with newly diagnosed tuberculosis patients. We compared social work students' tuberculosis knowledge, attitudes, and practices before and after their training. We assessed the change in number of tuberculosis diagnostic tests performed after implementation via an interrupted time series analysis with a quasi-Poisson regression model. We compared pre- and post-intervention probabilities of treatment initiation and completion using regression analyses, adjusting for potential baseline confounders. We conducted focus groups with the students, as well as brief surveys and one-on-one interviews with patients, to assess acceptability, feasibility, and implementation. RESULTS: During the study period, 1226 individuals received tuberculosis diagnostic testing and 163 patients started tuberculosis treatment, of whom 84 (51.5%) received individual counselling. The number of diagnostic tuberculosis tests performed increased by 1.36 (95%CI 1.23-1.58) times post-intervention, adjusting for background calendar trend. Probabilities of TB treatment initiation and treatment completion increased by 10.1% (95%CI 1.5-21.3%) and 4.4% (95%CI -7.3-16.0%), respectively. Patients found the counselling sessions alleviated anxiety and increased treatment self-efficacy. Social work students felt the clinic staff were collaborative and highly supportive of the intervention, and that it improved patient engagement and adherence. CONCLUSIONS: Engaging clinic staff in the development of an intervention ensures buy-in and collaboration. Education and counselling before and early-on in tuberculosis treatment can increase tuberculosis testing and treatment uptake. Training junior social workers can enable task-shifting in under-resourced settings, while addressing important service gaps in tuberculosis 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 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)0.044
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.033
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.066
GPT teacher head0.456
Teacher spread0.391 · 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 designQualitative
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

Citations8
Published2021
Admission routes2
Has abstractyes

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