“If you have a pain, get on a plane”: qualitatively exploring how short-term Canadian international retirement migrants prepare to manage their health while abroad
Bibliographic record
Abstract
BACKGROUND: Every year, tens of thousands of older Canadians travel abroad during the winter months to enjoy warmer destinations that offer social and recreational opportunities. How do these Canadians prepare to manage their health while abroad? In this analysis we explore this question by developing a typology of preparatory strategies. METHODS: Semi-structured interviews were conducted with 19 older Canadians living seasonally in Yuma, Arizona (United States). Interviews were transcribed verbatim and thematically analysed to form the basis of a typology of preparatory strategies. RESULTS: Four distinct preparatory strategies form the typology that summarizes how Canadian international retirement migrants prepare to manage their health while abroad. First, some participants became thoroughly prepared by gathering information from multiple sources and undertaking specific preparatory activities (e.g., visiting a travel medicine clinic, purchasing travel health insurance, bringing prescription refills). Second, some participants were preparation-adverse and relied on their abilities to address health needs and crises in-the-moment. Third, some participants became well informed about things they could do in advance to protect their health while abroad (e.g., purchasing travel health insurance) but opted not to undertake preparatory actions. A final group of participants prepared haphazardly. CONCLUSIONS: This typology can assist health care providers in international retirement migrant destinations to appreciate differences among this patient population that is often characterized as being relatively homogenous. More research is needed to determine if these preparatory strategies are common in other mobile populations and if they are found in other destinations popular with international retirement migrants.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".