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Record W2891579663 · doi:10.3917/spub.183.0383

Collaboration interprofessionnelle pour la santé buccodentaire des personnes âgées – esquisse de mise en œuvre

2018· article· fr· W2891579663 on OpenAlexaff
Hermina Harnagea

Bibliographic record

VenueSanté Publique · 2018
Typearticle
Languagefr
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsGovernment (linguistics)NursingGerontologyService (business)PopulationPopulation ageingPsychologyMedicineBusinessEnvironmental health

Abstract

fetched live from OpenAlex

Accelerated ageing of the population results in an ever-increasing number of functionally dependent elderly persons. Even healthy older people experience self-care deficits due to decreased mobility, endurance and sensory loss. Residents in long-term care centres depend on caregivers for the majority of their activities of daily living.Dental services for functionally dependent seniors generally remain inadequate. Several factors may explain this situation, including the absence of government guidelines and the lack of initial and continuing training of both dental and non-dental personnel.Based on a review of the scientific literature published between 1970-2016, four strategies were identified to improve this problem:Promoting, supporting and formalizing interprofessional collaboration.Building a common vision for oral health.Developing complementary and shared professional and relational skills.Redefining the roles and responsibilities of primary care practitioners.These strategies could be considered in the process of implementing a dental service adapted to the needs of this population group.

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)0.012
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.002
Scholarly communication0.0040.005
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.035
GPT teacher head0.449
Teacher spread0.414 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2018
Admission routes1
Has abstractyes

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