Innovations in providing and accessing preventative primary care for young children during COVID-19
Bibliographic record
Abstract
Context: The COVID-19 pandemic created many barriers for clinicians to deliver primary care and for parents/caregivers to access primary care for their young children. Research has shown that in some locations, primary care services were able to resume and recover to pre-pandemic visit rates for patients < 6 years old shortly after the initial lockdown in March/April 2020. Little is known, however, about what programs or services were implemented or adapted during the pandemic to facilitate the continuation of preventative primary care services for young children, including well-baby and well-child visits. Objective: To discover innovative programs or services that were adapted or created to help deliver and access primary care for children under the age of six years. Study Design and Analysis: A qualitative study design was employed using survey and interview methods. Data was analyzed using qualitative content analysis. Questions were structured to capture the delivery and access dimensions outlined in the Levesque conceptual framework for healthcare access. Setting: Ontario and Quebec, Canada. Populations Studied: Primary care providers who delivered care to young children and parents/caregivers of children who were under the age of six during the pandemic. Instrument: An online survey was distributed, and subsequent semi-structured telephone or video interviews were conducted between May and December 2023. Outcome Measures: Innovative primary care programs and services for children < 6 years old during the COVID-19 pandemic. Results: 102 individuals completed the on-line survey and of those, 19 participated in the interviews (13 parents and six primary care providers). Five over-arching themes arose from the data: 1) Clear decisional guidance, 2) Virtual care integration, 3) Clinic-level adaptations, 4) Proactive communication, and 5) Flexible policies. Conclusions: Participants’ experiences and ideas demonstrate that creativity and an openness to adapting can help continue established care, and even improve upon how care was previously provided, even when facing a healthcare crisis such as a global pandemic. The themes presented in this study stem from an overarching sentiment and shared desire to relieve decisional stress, and reduce the anxiety, conflict, frustration, and burden experienced by both those delivering and accessing primary care.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".