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Record W2805923897 · doi:10.5334/ijic.3587

Barriers and Facilitators to Workforce Changes in Integrated Care

2018· article· en· W2805923897 on OpenAlexaff
Loraine Busetto, Katrien Luijkx, Stefano Calciolari, Laura Guadalupe González Ortiz, Hubertus JM Vrijhoef

Bibliographic record

VenueInternational Journal of Integrated Care · 2018
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsPancreas Centre (Canada)
Fundersnot available
KeywordsEnthusiasmWorkforcePsychological interventionNursingIntegrated careHealth careQualitative researchMedicineMedical educationIntervention (counseling)PsychologyPolitical scienceSociology

Abstract

fetched live from OpenAlex

INTRODUCTION: The aim of the study is to investigate the barriers and facilitators to the implementation of workforce changes implemented as part of integrated chronic care interventions. METHODS: We used a qualitative multimethod design that combined expert questionnaires, a systematic literature review, and secondary analysis of two case reports. Twenty-five experts, twenty-one studies and two case reports were included in the study. RESULTS: Most barriers related to problematic delivery structures, health professionals' skills and enthusiasm, IT, funding, culture and cooperation and communication. Most facilitators related to health professionals' motivation and enthusiasm, good delivery structures, communication and cooperation, IT, patients, leadership and senior management. Overall, similar categories of barriers and facilitators were found. DISCUSSION: We recommend that future research focusses on more complex designs including multiple data sources, as these are better able to capture the complexity of interventions such as integrated care. We recommend that health managers and policy-makers should invest in delivery structures and skills and motivation of health professionals to improve the implementation of workforce changes in integrated chronic care interventions. CONCLUSION: The added value of the present study lies in its provision of information on which factors might mitigate the success of an intervention, which helps to prevent premature conclusions of ineffectiveness for complex interventions.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.020
GPT teacher head0.410
Teacher spread0.390 · 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 designQualitative
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

Citations52
Published2018
Admission routes1
Has abstractyes

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