Delay in Accessing Primary Health Care and its Impacts among Nepalese Immigrants Population in Canada
Bibliographic record
Abstract
Background: Equitable access to healthcare across the population, irrespective of social economic factors and area of residence, is crucial. Delay in seeking care may decrease care access, later diagnosis, and delayed or inadequate treatment of health conditions which leads to poor health outcomes. This study aimed to assess delay in accessing PHC and associated factors among Nepalese immigrants residing in Calgary. Methods: A cross-sectional study using a self-administered questionnaire was conducted in 2019. Delay in accessing care was measured based on a single-item question: “During the past 12 months, was there ever a time that you had to delay seeking medical service within any of the following services (multiple answers allowed)?” A follow-up question about associated factors was asked, and the responses were categorized into availability, accessibility, and acceptability. Descriptive and multivariable logistic regression was employed to assess the association between delay in accessing care and its predictors by using STATA. Results: Of 401 study participants, over two-thirds (n=266; 66.33%) reported that they had a care delay during the 12 months. Delay in accessing care was over two times higher among those aged 26-45 (AOR 2.98) compared to those under 25 years of age, which was nearly seven times higher (AOR 6.96) among the older participants (≥56 year). The top two areas of delaying accessing care were referral and related services (69.17%) and dental and related services (55.64%). The top three reasons reported were waiting time (77.82%), cost (55.64%), services availability (53.38%). Overall, accessibility (n=170, 63.91%), availability (n=225, 84.59%), and acceptability (n=135, 50.75%) were a barrier to access care. Those who reported delay in accessing care also reported an impact on their lives personally and economically. The most reported personal impact was mental health impact, including worry, anxiety, and stress (n=198, 74.72%), and the most common economic impact reported was increased use of over-the-counter drugs (n=114, 43.02%). Conclusion: Delay in accessing care is presented in the Nepalese immigrant population that impacts individuals’ personal health, daily life activities, and financial capacity. Strategies to improve access to PHC for deprived 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".