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Record W4381195676 · doi:10.12927/hcpol.2023.27090

“A Band-Aid Solution”: Policy Maker and Primary Care Provider Perspectives on the Value of Attachment Incentives

2023· article· en· W4381195676 on OpenAlexaffvenue
Emily Gard Marshall, Mackenzie R. Cook, Lauren Moritz, Richard Buote, Maria Mathews, Mylaine Breton

Bibliographic record

VenueHealthcare policy · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversité de SherbrookeWestern UniversityDalhousie University
Fundersnot available
KeywordsIncentiveRemunerationThematic analysisQualitative researchBusinessPrimary careNursingValue (mathematics)Health careEconomic shortageMedicinePublic relationsFamily medicineFinanceEconomic growthEconomicsPolitical scienceGovernment (linguistics)Sociology

Abstract

fetched live from OpenAlex

Approximately 15% of Canadians are without a primary care provider ("unattached"). To address "unattachment," several provinces introduced a financial incentive for family physicians who attach new patients. A descriptive qualitative approach was used to explore perspectives of patient access and attachment to primary care. Semi-structured qualitative interviews were conducted with family physicians, nurse practitioners and policy makers in Nova Scotia. Thematic analysis was performed to identify participant perspectives on the value and efficacy of financial incentives to promote patient attachment. Three themes were identified: (1) positive impacts of the incentive, (2) shortcomings of the incentive and (3) alternative strategies to strengthen primary healthcare. Participants felt that attachment incentives may offer short-term solutions to patient unattachment; however, financial incentives cannot overcome systemic challenges. Participants recommended alternative policy levers to strengthen primary healthcare, including addressing the shortage of primary care providers and developing remuneration and practice models that support sustainable patient attachment.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.640
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.063
GPT teacher head0.448
Teacher spread0.385 · 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.

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

Citations2
Published2023
Admission routes2
Has abstractyes

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