Perceptions of unmet healthcare needs: what do Punjabi and Chinese-speaking immigrants think? A qualitative study
Bibliographic record
Abstract
BACKGROUND: Unmet healthcare needs - the difference between healthcare services deemed necessary to deal with a particular health problem and the actual services received - is commonly measured by the question, "During the past 12 months, was there ever a time when you felt that you needed healthcare, but you didn't receive it?" In 2003, unmet needs were reported by 10% of immigrants in Canada, yet, little is known specifically about Chinese- or Punjabi-speaking immigrants' perceptions and reporting of unmet needs. Our study examined: 1) How are unmet healthcare needs conceptualized among Chinese- and Punjabi-speaking immigrants? 2) Are their primary healthcare experiences related to their unmet healthcare needs? METHODS: Twelve focus groups (6 Chinese, 6 Punjabi; n = 78) were conducted in Chinese or Punjabi and socio-demographic and health data were collected. Thematic analysis of focus group data examined the perceptions of unmet needs and any relationship to primary healthcare experiences. RESULTS: Our analysis revealed two overarching themes: 1) defining an unmet healthcare need and 2) identifying an unmet need. Participants had unmet healthcare needs in relation to barriers to accessing care, their lack of health system literacy, and when the health system was less responsive than their expectations. CONCLUSIONS: Asking whether someone ever had a time when they needed healthcare but did not receive it can either underestimate or overestimate unmet need. Measuring unmet need using single items is likely insufficient since more detail in a revised set of questions could begin to clarify whether the reporting of an unmet need was based on an expectation or a clinical need. Who defines what an unmet healthcare need is depends on the context (insured versus uninsured health services, experience in two or more healthcare systems versus experience in one healthcare system) and who is defining it (provider, patient, insurer).
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".