MétaCan
Menu
Back to cohort
Record W4400333675 · doi:10.1016/j.echu.2024.04.001

South African Chiropractic Students’ Intentions, Motivations, and Considerations for Emigration: A Cross-Sectional Study

2024· article· en· W4400333675 on OpenAlexaboutno aff
Fatima Ismail, Courtney Coetzee

Bibliographic record

VenueJournal of Chiropractic Humanities · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
FundersUniversity of Johannesburg
KeywordsEmigrationChiropracticPsychological interventionDestinationsMedicinePolitical scienceAlternative medicineNursing

Abstract

fetched live from OpenAlex

Objective: The purpose of this study was to investigate the emigration intentions of South African senior chiropractic students upon graduation, emphasizing motivations and considerations guiding migration decisions. Methods: = 177) between March 15 and May 19, 2021. Data were analyzed using frequencies, descriptions, and cross-tabulations to identify trends and interrelationships related to students' intentions to emigrate postqualification. Results: Findings indicate that 75.5% of South African chiropractic senior students intend to emigrate. Motivations for emigration include improved quality of life and seeking of opportunities. Economic instability in South Africa (SA) (82.7%) and concern for the National Health Insurance implementation (57.7%) serve as a significant push factor, whereas economic stability abroad (85.7%) emerged as a key pull factor. Preferred emigration destinations are primarily developed countries with established chiropractic communities. Conclusion: High emigration intentions among students were driven by diverse push factors in SA, including economic decline, socio-political climates, and safety concerns, contrasting with pull factors abroad, such as better opportunities, living conditions, and economic stability. Concerns regarding healthcare reforms, particularly the National Health Insurance, are also highlighted. Destinations in order of preference such as the United Kingdom, Canada, and Australia offer valuable insights for policy interventions. Understanding these dynamics is crucial for developing effective retention strategies and addressing socio-economic challenges.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.171
GPT teacher head0.478
Teacher spread0.307 · 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.

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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Chiropractic HumanitiesSame topicGlobal Health Workforce IssuesFrench-language works237,207