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Record W2601492187 · doi:10.15171/ijhpm.2017.04

A Process Evaluation to Assess Contextual Factors Associated With the Uptake of a Rapid Response Service to Support Health Systems’ Decision-Making in Uganda

2017· article· en· W2601492187 on OpenAlexfundno aff
Rhona Mijumbi, Nelson K. Sewankambo

Bibliographic record

VenueInternational Journal of Health Policy and Management · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersEuropean CommissionInternational Development Research Centre
KeywordsService (business)Thematic analysisProcess (computing)Control (management)BusinessKnowledge translationKnowledge managementProcess managementWork (physics)Affect (linguistics)Public relationsMarketingComputer scienceQualitative researchPolitical sciencePsychologyEngineeringSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Although proven feasible, rapid response services (RRSs) to support urgent decision and policymaking are still a fairly new and innovative strategy in several health systems, more especially in low-income countries. There are several information gaps about these RRSs that exist including the factors that make them work in different contexts and in addition what affects their uptake by potential end users. METHODS: We used a case study employing process evaluation methods to determine what contextual factors affect the utilization of a RRS in Uganda. We held in-depth interviews with researchers, knowledge translation (KT) specialists and policy-makers from several research and policy-making institutions in Uganda's health sector. We analyzed the data using thematic analysis to develop categories and themes about activities and structures under given program components that affected uptake of the service. RESULTS: We identified several factors under three themes that have both overlapping relations and also reinforcing loops amplifying each other: Internal factors (those factors that were identified as over which the RRS had full [or almost full] control); external factors (factors over which the service had only partial influence, a second party holds part of this influence); and environmental factors (factors over which the service had no or only remote control if at all). Internal factors were the design of the service and resources available for it, while the external factors were the service's visibility, integrity and relationships. Environmental factors were political will and health system policy and decision-making infrastructure. CONCLUSION: For health systems practitioners considering RRSs, knowing what factors will affect uptake and therefore modifying them within their contexts is important to ensure efficient use and successful utilization of the mechanisms.

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)0.123
metaresearch head score (Gemma)0.132
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 score0.123
Threshold uncertainty score0.649

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1230.132
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.004
Science and technology studies0.0040.003
Scholarly communication0.0060.004
Open science0.0020.007
Research integrity0.0010.002
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 head0.111
GPT teacher head0.478
Teacher spread0.366 · 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

Citations44
Published2017
Admission routes1
Has abstractyes

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