Unmet Primary Health Care Needs among Nepalese Immigrant Population in Canada
Bibliographic record
Abstract
Background: Immigrants represent over one-fifth (21.9%) of the Canadian population, which is an increasing trend. Primary care is a gateway to accessing the healthcare system for the majority of Canadians seeking medical services; however, Canada reported a growing shortage of healthcare providers, mainly primary care practitioners. Canadians, including immigrants, encounter many unmet healthcare needs due to various reasons. This study aimed to assess unmet healthcare (UHC) needs and associated factors among Nepalese immigrants residing in Calgary. Methods: A cross-sectional study using a self-administered questionnaire was conducted in 2019. UHC needs were measured based on a single-item question: “During the past 12 months, was there ever a time that you felt you needed medical help, but you did not receive it”. A follow-up question was asked to learn about associated unmet needs factors, and the responses were categorized into availability, accessibility, and acceptability. Descriptive and multivariable logistic regression was employed to assess the association between UHC needs and its predictors by using STATA version 14.2. Results: Of 401 study participants, nearly half of the participants (n = 187; 46.63%) reported UHC needs, which was not significantly different among male and female participants (p = 0.718). UHC needs were nearly two times higher among those aged 26–45 (AOR 1.93) and those ≥56 years (AOR 2.17) compared to those under 25 years of age. The top reasons reported for unmet needs were long waits to access care (67.91%), healthcare costs (57.22%), and lack of knowing where to get help (31.55%). Overall, “services availability when required” was a leading obstacle that accounted for UHC needs (n = 137, 73.26%). Nearly two-thirds (n = 121, 64.71%) of participants reported that “accessibility of services” was a barrier, followed by “acceptability (n = 107, 57.22%). Those who reported UHC needs also reported an impact on their lives personally and economically. The most commonly reported personal impact was mental health impact, including worry, anxiety, and stress (67.38%). The most common economic impact reported due to UHC needs was increased use of over-the-counter drugs (33.16%) and increased healthcare costs (17.20%). Conclusions: UHC needs are presented in the Nepalese immigrant population. Accessibility to healthcare is limited for several reasons: waiting time, cost, distance, and unavailability of services. UHC needs impact individuals’ personal health, daily life activities, and financial capacity. Strategies to improve access to PHC for disadvantaged populations are crucial and need to be tackled effectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".