MétaCan
Menu
Back to cohort
Record W2911987285 · doi:10.1186/s12992-018-0447-5

Health sector fragmentation: three examples from Sierra Leone

2019· review· en· W2911987285 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.

Bibliographic record

VenueGlobalization and Health · 2019
Typereview
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSierra leoneSocial policyPublic healthHealth services researchFragmentation (computing)Political scienceHealth policyGeographySociologyMedicineSocioeconomicsNursingBiologyLaw

Abstract

fetched live from OpenAlex

BACKGROUND: Fragmentation across governance structures, funding, and external actor engagement in Sierra Leone continues to challenge the efficiency and coherence of health sector activities and impedes sustained health system strengthening. Three examples are discussed to highlight the extent, causes, and impacts of health sector fragmentation in Sierra Leone: the community health worker programme, national medical supply chain, and service level agreements. RESULTS: In these examples we discuss factors contributing to fragmentation, the impact on efficiency of systems and sustainability of interventions, and persistent barriers to achieving sustainable improvements in health system performance. Prolonged external dependence and a proliferation of partner and donor involvement tending towards vertical programming and funding have contributed to this fragmentation. CONCLUSION: Alignment of policy and planning initiatives, investment in proactive (to reduce need for reactive) policy and plan development, strengthened partnerships, and strengthened governance and accountability mechanisms offer opportunities for greater health sector integration.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.943
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
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 head0.123
GPT teacher head0.402
Teacher spread0.279 · 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