Assessment of an Expert Committee as a Referral Process Within Health and Social Services
Bibliographic record
Abstract
Abstract In Québec, Canada, health and social services available to persons with intellectual disability are hierarchically organized into three levels of care: primary, secondary, and tertiary care. The referral processes through which persons gain access to services at each level vary across facilities. As a result, persons with intellectual disability may not receive the appropriate level of care and the responsibilities of facilities at each level overlap, creating an undue burden on the public healthcare system. This study sought to propose a structured assessment and referral method within the network of public services. Specifically, it evaluated the correspondence between the currently received level of care and an expert committee's determination. Furthermore, it examined client‐related variables that were associated with the level of services. An expert committee evaluated the level of specialization of services required by 30 persons with intellectual disability. The committee's determination was based on participants' files and presentations by their primary case worker. It was found that 10 out of the 30 participants were not receiving the level of care determined to be necessary by the expert committee. Challenging behaviors were most strongly associated with the committee's determination. This study underscores the primacy of clinical judgment, rather than a predetermined list of participant characteristics, in order to refer persons with intellectual disability toward services that best meet their specific needs. It also highlights the importance of taking into account challenging behaviors.
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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 | 0.053 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".