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Record W2020743592 · doi:10.3138/ptc.2010-45

Professional Satisfaction and Desire to Emigrate among Nigerian Physiotherapists

2011· article· en· W2020743592 on OpenAlexvenueno aff
Adetoyeje Y. Oyeyemi, Adewale L. Oyeyemi, Stanley Monday Maduagwu, Adamu Ahmad Rufa’i, Salamatu Umar Aliyu

Bibliographic record

VenuePhysiotherapy Canada · 2011
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsEmigrationRemunerationBrain drainMedicineJob satisfactionWork (physics)Developing countryNursingPsychologyBusinessDemographic economicsPolitical scienceSocial psychologyEconomic growthEngineering

Abstract

fetched live from OpenAlex

PURPOSE: Physiotherapists from developing countries are attracted to developed countries, where health personnel are in high demand. We investigated Nigerian physiotherapists' desire to emigrate, explored the possible relationship between job satisfaction and emigration, and elucidated common reasons why physiotherapists emigrate to other countries. METHODS: Nigerian physiotherapists (n=181) were surveyed using a three-part questionnaire. Part 1 elicited socio-demographic information; part 2 assessed satisfaction with work; and part 3 assessed the importance of some possible reasons that physiotherapists choose to emigrate. RESULTS: Close to half of the physiotherapists surveyed have plans to emigrate, but no relationship exists between job satisfaction level and desire to emigrate. An overwhelming majority felt that better or more realistic remuneration was the most important reason for them to leave their country, whereas age and practice experience were inversely related to physiotherapists' desire to emigrate. CONCLUSION: Policies aimed at mediating "brain drain" should take age and experience into consideration and should be geared toward creating opportunities for career advancement and continuing education.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.372
Teacher spread0.344 · 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

Citations16
Published2011
Admission routes1
Has abstractyes

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