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Record W3165254773 · doi:10.31234/osf.io/cmz62

An international fieldwork program in Sub-Saharan Africa for Japanese nursing students to learn intercultural competence for healthcare support,

2020· preprint· en· W3165254773 on OpenAlexaboutno aff
Lungwani Muungo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careWorkforcePopulationNursingDeveloping countryCompetence (human resources)Economic growthBusinessCultural competenceMedicinePolitical sciencePsychologyEnvironmental health

Abstract

fetched live from OpenAlex

The global population is growing continuously, particularly indeveloping countries without advanced social infrastructure.1) 4)Although the amount of service provided depends on the size ofthe aging population, industrialized countries with slowerpopulation growth offer citizens numerous opportunities toaccess various social services, including health care.4-6)Thisnon-uniform increase in the population causes global economicinequality, and the resulting medical needs in developingcountries, which are often considerable, may be ignored5) 7); insuch countries, personnel shortage attributed to healthworkermigrationis one of the main concerns.1) 4) Internationalcooperative support from non-governmental organizations,including academic institutions, is needed to solve theseproblems.The role of nurses, the largest workforce in the field of healthcare1) , has expanded from providing comfort care to postsurgicalpatients or those with incurable diseases, to participating inclinical research and advanced technology development.8)Nurses are expected to show leadership in overcoming healthcare challenges in developing economies3) and identify theirresponsibilities as global health diplomats.Thus, global nursingeducation must be promoted to give students more experienceof the health practices in developing countries.1) 3) 7)Japan, currently categorized as a developed country, mustlearn intercultural competence in nursing care. Japan’s NationalHealth Care Insurance provides all citizens with access toadvanced health care services. As a result, Japan’s lifeexpectancy at birth is one of the highest, and increasingly bettersocial services and more personnel are required to care for thegrowing population of elderly citizens.4) 6) The development of areliable home health care system and building more nursingschools are encouraged to meet the future demand, althoughthe nursing shortage must still be solved. Another way to bringin new personnel may be the recruitment of non-Japanesepeople from other countries; individuals would receive trainingunder the supervision of Japanese staff before beginning work.Good teamwork between professionals from various culturaland language backgrounds is essential to enhanced performancein care.9) 10) In multicultural societies such as the United Statesand Canada, intercultural competence training is recommendedfor the improvement of health professional skills.11) 12) Understandingdifferences in social values requires time; therefore,comprehensive education is necessary for global nursingpartnership. Although Japan is not considered a multiculturalcountry, a diverse range of cultures, values, and traditions stillexist in it, and the realization and acceptance of such values isrequired. Intercultural competence may be associated with goodhealth care support for Japanese seniors.13) In many Japanesenursing schools, global issues are gradually being incorporatedinto the curriculum14) ; acquiring skills for culturally competentnursing care may be important not only for global cooperationbut also to provide better healthcare for the aging population ofJapan.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.049
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0070.001
Scholarly communication0.0020.001
Open science0.0020.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0490.009

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 head0.117
GPT teacher head0.468
Teacher spread0.351 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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