Part III: Occupational Disability Determination/Rehabilitation Best Practices and the Role of Situational Work Assessment and Simulated Work/Academic Trials.
Bibliographic record
Abstract
This is the final in a series of three papers addressing common occupational disability entitlements from an Ontario motor vehicle accident (MVA) perspective, which are also applicable to long-term disability cases. The first paper supported using a holistic model in the assessment of accident injured persons who are unable to return to the pre-accident occupation (pre- 104 disability/"own occupation") because of accident-caused impairments. The second paper discussed case law, best practice and the Post-104 Week IRB Disability ("any occupation") test and demonstrated the difficulties individuals face when they are unable to return to work in the aftermath of a debilitating motor vehicle accident. In this third and final paper, the purposes and roles of the Situational Work Assessment and Simulated Work/Academic Trials are critically evaluated in the occupational disability context. These methodologies are used to determine an individual's capacity to competitively meet the physical, cognitive and interpersonal/behavioural demands of his or her pre-condition occupation, or any occupation for which he or she is suited by education, training or experience, thereby addressing entitlement to income replacement benefits (IRB). It is vital that occupational disability assessments are comprehensive, holistic, include an undestanding of the synergistic impact of the impairment on the individual's physical, cognitive and psychosocial work capacities, and are conducted through multi-modal means. To conclude, the overriding principles and themes of all three articles are synthesized.
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 | 0.022 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".