Refugee smartphone access to health care in Canada: Concept analysis
Bibliographic record
Abstract
Objective: With the ever-changing smartphone healthcare technology also comes nurses’ responsibilities to recognize its ethical implications particularly among vulnerable population. The aim of this paper is to explore what we know about the use of smartphone to access health care among refugees and new immigrants.Methods: We were guided by Walker and Avant (2011) concept analysis methodology. Concept analysis is a rigorous method to better understand ethical implications, meaning, attributes, antecedents and consequences of smartphone access to health care. Diverse databases were included such as CINAHL, Journals@Ovid, ProQuest Nursing & Allied Health Source, ProQuest Psychology Journals, PsychINFO, ERIC, and Education Full Text.Results: The concept analysis retrieved 23 studies. Overarching themes included the physical (e.g. income, geographical location) and social (generation; access to regular internet; digital literacy; relationship with practitioners) that were attributed to refugee and new immigrant access to health care.Conclusions: Some of the ethical implication when using smartphone to access health care technology with refugees and new immigrants are discussed and the skills needed for nursing practice are identified and recommendations for nurse education and research are made.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".