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Record W2956834976 · doi:10.1186/s12960-019-0393-1

Medical diaspora: an underused entity in low- and middle-income countries’ health system development

2019· article· en· W2956834976 on OpenAlexaboutno aff
Seble Frehywot, Chulwoo Park, Alexandra C Infanzon

Bibliographic record

VenueHuman Resources for Health · 2019
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsSocial policyHealth services researchHealth administrationLow and middle income countriesHealth informaticsDiasporaPublic healthHealth economicsHealth policyEconomic growthBusinessMedicineDeveloping countryPolitical scienceNursingEconomics

Abstract

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BACKGROUND: At present, over 215 million people live outside their countries of birth, many of which are referred to as diaspora-those that live in host countries but maintain strong sentimental and material links with their countries of origin, their homelands. The critical shortage of Human Resources for Health (HRH) in many developing countries remains a barrier to attaining their health system goals. Usage of medical diaspora can be one way to meet this need. A growing number of policy-makers have come to acknowledge that medical diaspora can play a vital role in the development of their homeland's health workforce capacity. To date, no inventory of low- and middle-income countries (LMIC) medical diaspora organizations has been done. This paper intends to develop an inventory that is as complete as possible, of the names of the LMIC medical diaspora organizations in the United States of America, the United Kingdom, Canada, and Australia and addresses their interests and roles in building the health system of their country of origin. METHODS: The researchers utilized six steps for their research methodology: (1) development of rationale for choosing the four destination countries (the United States of America, the United Kingdom, Canada, and Australia); (2) identification of low- and middle-income countries (LMIC); (3) web search for the name of LMIC medical diaspora organization in the United States of America, the United Kingdom, Canada, and Australia through the search engines of PubMed, Scopus, Google, Google Scholar, and LexisNexis; (4) development of inclusion and exclusion criteria and creation of a medical diaspora organizations' inventory list (Table 1) and corresponding maps (Figures 1, 2, and 3). Using decision criteria, reviewers narrowed the number to a final 89 organizations; (5) synthesis of information to collect the general as well as the unique roles the medical diaspora organizations play in building health systems; and (6) developing inventory of respective LMIC governments' diaspora offices (Table 2) to identify units/departments that facilitate diaspora's work. RESULT: In total, the authors found 89 medical diaspora organizations in 4 main countries: in the United States of America 60, in the United Kingdom 24, in Australia 3, and in Canada 2. These medical diaspora organizations tend to have three focuses: providing healthcare services, training, and when needed humanitarian aid to their home country; creating a social or professional network of migrant physicians (i.e., simply to bring together people with an ethnic and professional commonality) and; supplying improved and culturally sensitive healthcare to the migrant population within the host country. Sixty-eight LMIC countries have established a diaspora office within their government office. It is also equally important to note that many policy-makers may lack knowledge of models for medical diaspora engagement or of valuable lessons learned by other governments about working with diaspora. CONCLUSIONS: The medical diaspora remains an underutilized resource in both health systems policy formulation and program implementation.

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)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.005
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.049
GPT teacher head0.412
Teacher spread0.363 · 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 designTheoretical or conceptual
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

Citations24
Published2019
Admission routes1
Has abstractyes

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