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Record W4387865729 · doi:10.1097/pxr.0000000000000297

A phenomenological analysis of medical tourism: Investigating the lived experience of returning to Canada after osseointegration abroad

2023· article· en· W4387865729 on OpenAlexaffabout
Christopher D. Bedore, A. Kirsten Woodend

Bibliographic record

VenueProsthetics and Orthotics International · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsTrent University
Fundersnot available
KeywordsOsseointegrationInterpretative phenomenological analysisTourismPhenomenology (philosophy)Lived experiencePsychologyMedicineSociologyAestheticsGerontologyQualitative researchPsychotherapistGeographyArtSurgeryEpistemologySocial sciencePhilosophy

Abstract

fetched live from OpenAlex

BACKGROUND: Travel out of country for medical care is increasing for many reasons including costs, wait-lists, and availability of procedures. Until 2018, when the surgery was offered in only 1 province, Canadians with amputation wanting osseointegration (OI) had to travel out of country for the surgery. The purpose of this study was to understand the lived experience of accessing health care in Canada after having a procedure performed out of country. METHODS: This is a phenomenological study of persons with amputation who had OI outside of Canada. The grand tour interview question was "What was it like travelling to another country for OI surgery and then returning to access follow-up care in Canada?" RESULTS: There were 5 participants, and 5 themes emerged: (1) lack of support from Canadian physicians; (2) exceptional support from prosthetists and other members of the health care team; (3) continued reliance on the country where the procedure was initially performed; (4) self-advocacy for access to care; and (5) benefits of travelling outweighing the problems faced. CONCLUSIONS: These themes are not unique to OI, but to medical tourism as a whole. The lack of support was countered partly by a strong sense of self-advocacy from the participants and support from other members of the health care team.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.098
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.074
GPT teacher head0.437
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 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

Citations1
Published2023
Admission routes2
Has abstractyes

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