[Development of decision support guidance of people in loss of autonomy in the care system in the Canton of Geneva's healthcare system].
Bibliographic record
Abstract
AIM: The objective of this study is to use Iso-SMAF functional autonomy profiles (SMAF: French acronym for Functional autonomy measurement system) and other clinical and sociodemographic characteristics to develop algorithms that would support decision-making in orienting disabled people that would be adapted to current and future options available to a healthcare system in the Canton of Geneva. METHODS: A modified Delphi expert consultation, including the RAND/UCLA method, was used. After two rounds of consultations in which the experts responded to Web-based questionnaires, a nominal group completed the study. RESULTS: A total of 108 experts took part in the online consultation during the two rounds of consultation; 13 took part in the nominal group. In addition to belonging to various disciplines, the participants came from the different care structures within the Canton of Geneva. The findings made it possible to develop 14 decision-making algorithms, eight of which received consensus support in the first round of consultation. The second round and nominal group achieved consensus for others. The profiles with moderate to severe cognitive impairment were those for which the orientation options required several rounds of voting. CONCLUSION: Since the orientation choices suggested by the algorithms were relatively standardized, they constitute recommendations that can guide professionals in orienting people.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".