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Record W4311528924 · doi:10.29392/001c.55762

Resilience of the medical mission model: assessment of the perceived impact of the COVID-19 pandemic on short-term medical missions to Latin America and the Caribbean

2022· article· en· W4311528924 on OpenAlexaff
Christopher Dainton, Ghazal Jessani, Caroline Hircock

Bibliographic record

VenueJournal of Global Health Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLatin AmericansPandemicPsychological resilienceResilience (materials science)OptimismCoronavirus disease 2019 (COVID-19)Flexibility (engineering)Health careSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)BusinessPsychologyMedical educationPublic relationsPolitical scienceFamily medicineMedicineManagementSocial psychology

Abstract

fetched live from OpenAlex

Background COVID-19-related travel restrictions profoundly impacted short-term medical missions (STMMs) abroad. This study describes the effect of the pandemic as perceived by STMM organisations serving Latin America and the Caribbean (LAC). Methods Information was updated for 359 primary care STMM organisations previously active in 2015, which were contained within an existing online database. Organisations were contacted to complete an online survey that gathered quantitative and qualitative descriptions of pandemic-related adaptations and program changes. Results 22.5% (73/324) of previously active organisations had no website activity since 2020 or earlier, no longer existed, or had unclear website activity. Eighty-seven organisations responded to the survey. Ninety-six percent indicated that they would definitely (72/86, 84%) or probably (10/86, 12%) return to sending STMMs in the next five years, and most (46/83, 55%) of these intended to send an STMM within the next six months. Seventy-two respondents (93%) reported new adaptations, including direct funding for local healthcare professionals, sending equipment to host communities, focusing on training and teaching, and incorporating virtual care and electronic medical records. Conclusions The results demonstrate resilience, flexibility and optimism among STMM organisations and an intent to return to pre-pandemic programming rapidly.

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)0.006
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.045
GPT teacher head0.449
Teacher spread0.404 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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