Use of a Psychometric Instrument as a Referral Process for the Required Level of Specialization of Health and Social Services
Bibliographic record
Abstract
Abstract In Québec, services of people with intellectual disability (ID) are divided into three levels of care based on the World Health Organization model. Currently, no standardized tool is used to assess the level of specialization of care required by persons with ID in the province despite the Ministry of Social and Health Services' reference framework, which stipulates that services should be fair and easily accessible to all. The present study examined potential tools to address this situation. The scores on the Supports Intensity Scale‐French version (SIS‐F), Adaptive Behavior Assessment System‐II, and Scales of Independent Behavior‐Revised (SIB‐R) (Part 2) of 30 participants with ID were examined in conjunction with the required level of specialization of services, as determined by an expert committee. Scores on these scales were not linearly related to the required level of care. This indicates that scores cannot be used to determine if a person needs services from the primary, secondary, or tertiary care facility. However, significant differences were observed between the primary and tertiary levels on the Exceptional Behavioral Support Needs scale of the SIS‐F and the SIB‐R Part 2. The study shows that the expert committee was more successful in making this determination than a standardized instrument. The instruments used in the present study, not designed for this purpose, were insufficient. Nonetheless, results underline the importance of factoring in challenging behaviors in the assessment of service needs.
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 | 0.034 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".