Innovations that helped with accessing and delivering primary care for infants and young children during COVID-19
Bibliographic record
Abstract
BACKGROUND: For children under age six, regular preventative primary care is needed for administration of vaccinations, surveillance of development, and early diagnosis and intervention for any potential health conditions or developmental delays. The COVID-19 pandemic created many barriers to providing and accessing primary care. While many studies have explored these barriers, it is important to understand how primary care adapted to ensure these crucial early-years appointments were not missed throughout the pandemic. The objective of this study, therefore, was to discover innovative programs or services that were used, and suggestions for programs or services that could have been implemented, to facilitate delivery of, and access to, primary care for young children during the COVID-19 pandemic (March 2020-May 2023). METHODS: This qualitative descriptive study used a survey and semi-structured interviews to discuss primary care programs or services with parents or caregivers of children who were under the age of 6 during the pandemic and with primary care providers who deliver care to young children. The survey was sent to parent groups and primary care providers across the Canadian provinces of Ontario and Quebec from May to October 2023. Survey participants who indicated interest in further participation were subsequently contacted for an interview. Results were analyzed using content and thematic analyses. RESULTS: 102 individuals (33 primary care providers and 69 parents or caregivers) responded to the survey and of those, 19 participated in the interviews. Six themes emerged from the innovative services or procedures that were discussed: Prioritization of young children; Creation or modification of primary care spaces; Clear decision-making guidelines; Virtual care integration; Proactive communication; and Interdisciplinary collaboration. CONCLUSIONS: This qualitative study explored some creative and positive solutions to the struggle of providing and accessing primary care for young children during the COVID-19 pandemic. The results from this research highlight the importance of flexibility and innovation within the primary care system, especially under circumstances of sudden and unexpected increases in barriers to providing and accessing care. On-going development of innovations that improve communication, take a more collaborative approach, and adapt systems, spaces, and methods will improve primary care access and delivery. CLINICAL TRIAL NUMBER: Not applicable.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".