Healthcare Needs of Provincially Uninsured Migrant Families with Newborn Infants: A Qualitative Descriptive Study
Bibliographic record
Abstract
Aim: This project aimed to explore the healthcare needs of provincially uninsured migrant families with newborns receiving care at a nurse practitioner (NP)-led clinic in Montreal, Quebec, Canada. The findings of this study are intended to guide primary healthcare professionals and administrators in understanding and addressing the specific needs of these families, enabling them to tailor their services appropriately. Background: Newborns of provincially uninsured migrant families in Quebec can experience delays in accessing provincial health insurance, preventing timely access to primary care services. In Montreal, a publicly funded NP-led clinic was created to enable newborns without provincial health insurance to access recommended well-baby visits in the first 4 months of life. There exists a paucity of literature on the specific healthcare needs of uninsured families with newborn infants, making tailored care difficult. Methods: A qualitative descriptive study was undertaken. Semi-structured interviews were conducted with the parents of provincially uninsured Quebec-born newborns being treated at a NP-led clinic. Ten interviews were conducted with a total of 13 participants between October 2023 and January 2024. Socio-demographic data were collected. Content analysis was undertaken to identify important themes. Findings: Two overarching themes were identified including (a) healthcare service needs related to newborn care, maternal postpartum care, and family healthcare; (b) healthcare access needs related to navigating the local healthcare system, transportation, and provincial health insurance. Conclusion: Provincially uninsured migrant families with newborns in Quebec face barriers to accessing healthcare. This study provides information on the healthcare services and access-related needs of these families and provides recommendations including how NP-led clinics can facilitate access to the healthcare services needed by these families.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".