MétaCan
Menu
← Back to cohort
Record W4392756505 · doi:10.1186/s12913-024-10803-9

Mid-level managers’ perspectives on implementing isoniazid preventive therapy for people living with HIV in Ugandan health districts: a qualitative study

2024· article· en· W4392756505 on OpenAlexfundno aff
Canice Christian, Elijah Kakande, Violah Nahurira, Cecilia Akatukwasa, Fredrick Atwine, Robert Bakanoma, Harriet Itiakorit, Asiphas Owaraganise, William DiIeso, Derek Rast, Jane Kabami, Jason Johnson Peretz, Starley B. Shade, Moses R. Kamya, Diane V. Havlir, Gabriel Chamie, Carol S. Camlin

Bibliographic record

VenueBMC Health Services Research · 2024
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesInternational Development Research Centre
KeywordsMedicineQualitative researchHealth administrationPsychological interventionPublic healthFocus groupIntervention (counseling)NursingHealth services researchImplementation researchFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Isoniazid preventive therapy (IPT) works to prevent tuberculosis (TB) among people living with HIV (PLHIV), but uptake remains low in Sub-Saharan Africa. In this analysis, we sought to identify barriers mid-level managers face in scaling IPT in Uganda and the mechanisms by which the SEARCH-IPT trial intervention influenced their abilities to increase IPT uptake. METHODS: The SEARCH-IPT study was a cluster randomized trial conducted from 2017-2021. The SEARCH-IPT intervention created collaborative groups of district health managers, facilitated by local HIV and TB experts, and provided leadership and management training over 3-years to increase IPT uptake in Uganda. In this qualitative study we analyzed transcripts of annual Focus Group Discussions and Key Informant Interviews, from a subset of SEARCH-IPT participants from intervention and control groups, and participant observation field notes. We conducted the analysis using inductive and deductive coding (with a priori codes and those derived from analysis) and a framework approach for data synthesis. RESULTS: When discussing factors that enabled positive outcomes, intervention managers described feeling ownership over interventions, supported by the leadership and management training they received in the SEARCH-IPT study, and the importance of collaboration between districts facilitated by the intervention. In contrast, when discussing factors that impeded their ability to make changes, intervention and control managers described external funders setting agendas, lack of collaboration in meetings that operated with more of a 'top-down' approach, inadequate supplies and staffing, and lack of motivation among frontline providers. Intervention group managers mentioned redistribution of available stock within districts as well as between districts, reflecting efforts of the SEARCH-IPT intervention to promote between-district collaboration, whereas control group managers mentioned redistribution within their districts to maximize the use of available IPT stock. CONCLUSIONS: In Uganda, mid-level managers' perceptions of barriers to scaling IPT included limited power to set agendas and control over funding, inadequate resources, lack of motivation of frontline providers, and lack of political prioritization. We found that the SEARCH-IPT intervention supported managers to design and implement strategies to improve IPT uptake and collaborate between districts. This may have contributed to the overall intervention effect in increasing the uptake of IPT among PLHIV compared to standard practice. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03315962 , Registered 20 October 2017.

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)0.018
metaresearch head score (Gemma)0.022
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0100.008
Scholarly communication0.0050.004
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.123
GPT teacher head0.532
Teacher spread0.409 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueBMC Health Services Research→Same topicTuberculosis Research and Epidemiology→French-language works237,207→