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Record W4390814416 · doi:10.1186/s12875-023-02240-0

Barriers and enablers to implementing interprofessional primary care teams: a narrative review of the literature using the consolidated framework for implementation research

2024· review· en· W4390814416 on OpenAlexafffundabout
Amy Grant, Julia Kontak, Elizabeth Jeffers, Beverley Lawson, Adrian MacKenzie, Fred Burge, Leah Boulos, Kelly Lackie, Emily Gard Marshall, Amy Mireault, Susan Philpott, Tara Sampalli, Debbie Sheppard-LeMoine, Ruth Martin‐Misener

Bibliographic record

VenueBMC Primary Care · 2024
Typereview
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of WindsorNova Scotia Department of Health and WellnessCancer Care Nova ScotiaNova Scotia Health AuthorityDalhousie University
FundersCanadian Institutes of Health ResearchDepartment of Health, Western Cape GovernmentNova Scotia Department of Health and WellnessFondation de la recherche en santé du Nouveau-Brunswick
KeywordsImplementation researchHealth careGovernment (linguistics)Knowledge managementGrey literatureMedicineMedical educationNursingMEDLINEPolitical scienceComputer sciencePsychological intervention

Abstract

fetched live from OpenAlex

BACKGROUND: Interprofessional primary care teams have been introduced across Canada to improve access (e.g., a regular primary care provider, timely access to care when needed) to and quality of primary care. However, the quality and speed of team implementation has not kept pace with increasing access issues. The aim of this research was to use an implementation framework to categorize and describe barriers and enablers to team implementation in primary care. METHODS: A narrative review that prioritized systematic reviews and evidence syntheses was conducted. A search using pre-defined terms was conducted using Ovid MEDLINE, and potentially relevant grey literature was identified through ad hoc Google searches and hand searching of health organization websites. The Consolidated Framework for Implementation Research (CFIR) was used to categorize barriers and enablers into five domains: (1) Features of Team Implementation; (2) Government, Health Authorities and Health Organizations; (3) Characteristics of the Team; (4) Characteristics of Team Members; and (5) Process of Implementation. RESULTS: Data were extracted from 19 of 435 articles that met inclusion/exclusion criteria. Most barriers and enablers were categorized into two domains of the CFIR: Characteristics of the Team and Government, Health Authorities, and Health Organizations. Key themes identified within the Characteristics of the Team domain were team-leadership, including designating a manager responsible for day-to-day activities and facilitating collaboration; clear governance structures, and technology supports and tools that facilitate information sharing and communication. Key themes within the Government, Health Authorities, and Health Organizations domain were professional remuneration plans, regulatory policy, and interprofessional education. Other key themes identified in the Features of Team Implementation included the importance of good data and research on the status of teams, as well as sufficient and stable funding models. Positive perspectives, flexibility, and feeling supported were identified in the Characteristics of Team Members domain. Within the Process of Implementation domain, shared leadership and human resources planning were discussed. CONCLUSIONS: Barriers and enablers to implementing interprofessional primary care teams using the CFIR were identified, which enables stakeholders and teams to tailor implementation of teams at the local level to impact the accessibility and quality of primary care.

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)0.061
metaresearch head score (Gemma)0.137
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.061
Threshold uncertainty score0.325

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0610.137
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0190.020
Science and technology studies0.0030.003
Scholarly communication0.0080.008
Open science0.0020.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.099
GPT teacher head0.571
Teacher spread0.471 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations52
Published2024
Admission routes3
Has abstractyes

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