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Record W2953168289 · doi:10.1111/ans.15295

Delivery of surgical care in Samoa: perspectives on capacity, barriers and opportunities by local providers

2019· article· en· W2953168289 on OpenAlexaff
Ben Comery, William Perry, S. Young, Anna Dare, Ben Matalavea, Ian Bissett, John A. Windsor

Bibliographic record

VenueANZ Journal of Surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of Toronto
FundersUniversity of AucklandWorld Health Organization
KeywordsMedicineChecklistEconomic shortageNursingHealth careNeeds assessmentGovernment (linguistics)

Abstract

fetched live from OpenAlex

BACKGROUND: The Pacific Island nation of Samoa faces a number of challenges in delivering surgical care. Our group aimed to identify the barriers and opportunities to improving the delivery of safe, affordable, timely surgical care in Samoa. METHODS: A mixed-methods approach was undertaken. The quantitative analysis used a modified version of the World Health Organization Emergency and Essential Surgical Checklist while the qualitative methodology used semi-structured interviews. Respondents were asked to share their views on the capacity, quality, accessibility and future directions of surgery in Samoa. Interviews were transcribed and analysed using open and axial coding techniques. RESULTS: Stakeholders had a positive outlook on the delivery of surgical care, but it was suggested that existing services were not meeting needs. Respondents cited limited access to equipment and resources, compounded by insufficient organizational and logistical infrastructure. Shortage of medical staff and retention was identified as a key issue. Shortcomings in primary care and poor health literacy were seen as significant barriers to accessing care. CONCLUSION: Documenting locally identified barriers and solutions to surgical care in Samoa is an important first step towards the development of formal strategies for improving surgical services nationally.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.032
GPT teacher head0.267
Teacher spread0.234 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2019
Admission routes1
Has abstractyes

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