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Nurse practitioners, canaries in the mine of primary care reform

2016· article· en· W2328708996 on OpenAlexaffabout
Damien Contandriopoulos, Astrid Brousselle, Mylaine Breton, Esther Sangster‐Gormley, Kelley Kilpatrick, Carl‐Ardy Dubois, Isabelle Brault, Mélanie Perroux

Bibliographic record

VenueHealth Policy · 2016
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsHôpital Charles-Le MoyneHôpital Maisonneuve-RosemontUniversity of VictoriaUniversité de SherbrookeUniversité de Montréal
Fundersnot available
KeywordsPrimary careNursingHealth care reformPrimary health careNurse practitionersMedicinePsychologyPolitical scienceFamily medicineHealth careHealth policyPublic healthLaw

Abstract

fetched live from OpenAlex

A strong and effective primary care capacity has been demonstrated to be crucial for controlling costs, improving outcomes, and ultimately enhancing the performance and sustainability of healthcare systems. However, current challenges are such that the future of primary care is unlikely to be an extension of the current dominant model. Profound environmental challenges are accumulating and are likely to drive significant transformation in the field. In this article we build upon the concept of "disruptive innovations" to analyze data from two separate research projects conducted in Quebec (Canada). Results from both projects suggest that introducing nurse practitioners into primary care teams has the potential to disrupt the status quo. We propose three scenarios for the future of primary care and for nurse practitioners' potential contribution to reforming primary care delivery models. In conclusion, we suggest that, like the canary in the coal mine, nurse practitioners' place in primary care will be an indicator of the extent to which healthcare system reforms have actually occurred.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.108
Threshold uncertainty score0.214

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.009
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.061
GPT teacher head0.466
Teacher spread0.404 · 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 source (direct Gemma or distilled Codex), 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

Citations79
Published2016
Admission routes2
Has abstractyes

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