MétaCan
Menu
Back to cohort
Record W4281712886 · doi:10.1186/s12913-022-08140-w

COVID-19 – an opportunity to improve access to primary care through organizational innovations? A qualitative multiple case study in Quebec and Nova Scotia (Canada)

2022· article· en· W4281712886 on OpenAlexafffundabout
Mylaine Breton, Emily Gard Marshall, Véronique Deslauriers, Mélanie Ann Smithman, Lauren Moritz, Richard Buote, Bobbi Morrison, Erin Christian, Madeleine McKay, Katherine Stringer, Claire Godard‐Sebillotte, Nadia Sourial, Maude Laberge, Adrian MacKenzie, Jennifer E. Isenor, Arnaud Duhoux, Rachelle Ashcroft, Maria Mathews, Benoît Cossette, Catherine Hudon, Beth McDougall, Line Guénette, Rhonda Kirkwood, Michael Green

Bibliographic record

VenueBMC Health Services Research · 2022
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsQueen's UniversityCollege of Physicians and Surgeons of OntarioUniversité de SherbrookeUniversity of TorontoWestern UniversityUniversité LavalUniversité de MontréalMcGill UniversityNova Scotia HospitalNova Scotia Health AuthoritySt. Francis Xavier UniversityDalhousie University
FundersCanadian Institutes of Health Research
KeywordsContext (archaeology)Public healthThematic analysisNursing researchPandemicMedicineHealth administrationHealth informaticsNursingHealth careQualitative researchFamily medicinePublic relationsPolitical scienceCoronavirus disease 2019 (COVID-19)GeographySociology

Abstract

fetched live from OpenAlex

BACKGROUND: COVID-19 catalyzed a rapid and substantial reorganization of primary care, accelerating the spread of existing strategies and fostering a proliferation of innovations. Access to primary care is an essential component of a healthcare system, particularly during a pandemic. We describe organizational innovations aiming to improve access to primary care and related contextual changes during the first 18 months of the COVID-19 pandemic in two Canadian provinces, Quebec and Nova Scotia. METHODS: We conducted a multiple case study based on 63 semi-structured interviews (n = 33 in Quebec, n = 30 in Nova Scotia) conducted between October 2020 and May 2021 and 71 documents from both jurisdictions. We recruited a diverse range of provincial and regional stakeholders (e.g., policy-makers, decision-makers, family physicians, nurses) involved in reorganizing primary care during the COVID-19 pandemic using purposeful sampling (e.g., based on role, region). Interviews were transcribed verbatim and thematic analysis was conducted in NVivo12. Emerging results were discussed by team members to identify salient themes and organized into logic models. RESULTS: We identified and analyzed six organizational innovations. Four of these - centralized public online booking systems, centralized access centers for unattached patients, interim primary care clinics for unattached patients, and a community connector to health and social services for older adults - pre-dated COVID-19 but were accelerated by the pandemic context. The remaining two innovations were created to specifically address pandemic-related needs: COVID-19 hotlines and COVID-dedicated primary healthcare clinics. Innovation spread and proliferation was influenced by several factors, such as a strengthened sense of community amongst providers, decreased patient demand at the beginning of the first wave, renewed policy and provider interest in population-wide access (versus attachment of patients only), suspended performance targets (e.g., continuity ≥80%) in Quebec, modality of care delivery, modified fee codes, and greater regional flexibility to implement tailored innovations. CONCLUSION: COVID-19 accelerated the uptake and creation of organizational innovations to potentially improve access to primary healthcare, removing, at least temporarily, certain longstanding barriers. Many stakeholders believed this reorganization would have positive impacts on access to primary care after the pandemic. Further studies should analyze the effectiveness and sustainability of innovations adapted, developed, and implemented during the COVID-19 pandemic.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.233
GPT teacher head0.547
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueBMC Health Services ResearchSame topicTelemedicine and Telehealth ImplementationFrench-language works237,207