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Record W1739043266 · doi:10.5334/ijic.1201

Adapting the Quebecois method for assessing implementation to the French National Alzheimer Plan 2008–2012: lessons for gerontological services integration

2014· article· en· W1739043266 on OpenAlexaboutno aff
Dominique Somme, Hélène Trouvé, Catherine Périsset, Aline Corvol, Joël Ankri, Olivier Saint‐Jean, Matthieu de Stampa

Bibliographic record

VenueInternational Journal of Integrated Care · 2014
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Multidisciplinary approachIntegrated careInstitutionalisationAutonomyProcess managementPlan (archaeology)Process (computing)Test (biology)MedicineNursingPsychologyComputer sciencePolitical scienceBusinessHealth carePsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Many countries face ageing-related demographic and epidemiological challenges, notably neurodegenerative disorders, due to the multiple care services they require, thereby pleading for a more integrated system of care. The integrated Quebecois method issued from the Programme of Research to Integrate Services for the Maintenance of Autonomy inspired a French pilot experiment and the National Alzheimer Plan 2008-2012. Programme of Research to Integrate Services for the Maintenance of Autonomy method implementation was rated with an evaluation grid adapted to assess its successive degrees of completion. DISCUSSION: The approaching end of the president's term led to the method's institutionalization (2011-2012), before the implementation study ended. When the government changed, the study was interrupted. The results extracted from that 'lost' study (presented herein) have, nonetheless, 'found' some key lessons. KEY LESSONS/CONCLUSION: It was possible to implement a Quebecois integrated-care method in France. We describe the lessons and pitfalls encountered in adapting this evaluation tool. This process is necessarily multidisciplinary and requires a test phase. A simple tool for quantitative assessment of integration was obtained. The first assessment of the tool was unsatisfactory but requires further studies. In the meantime, we recommend using mixed methodologies to assess the services integration level.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.875
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.297
GPT teacher head0.538
Teacher spread0.241 · 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 designOther design
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

Citations7
Published2014
Admission routes1
Has abstractyes

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