Common Occupational Disability Tests and Case Law References: An Ontario MVA perspective on interpretation and best practice methodology supporting a holistic model, Part I of III (Pre-104 IRB).
Bibliographic record
Abstract
This three-part paper presents practical holistic models of determining impairment and occupational disability with respect to common "own occupation" and "any occupation" definitions. The models consider physical, emotional and cognitive impairments in unison, and draw upon case law support for empirically based functional assessment of secondary cognitive symptoms arising from psychological conditions, including chronic pain disorders. Case law is presented, primarily in the context of Ontario motor vehicle accident legislation, to demonstrate how triers of fact have addressed occupational disability in the context of chronic pain; and interpreted the "own occupation" and "any occupation" definitions. In interpreting the definitions of "own occupation" and "any occupation", courts have considered various concepts, such as: work as an integrated whole, competitive productivity, demonstrated job performance vs. employment, work adaptation relative to impairment stability, suitable work, retraining considerations, self-employment, and remuneration/socio-economic status. The first segment of the paper reviews the above concepts largely in the context of pre-104 Income Replacement Benefit (IRB) entitlement, while the second segment focuses on post-104 IRB entitlement. In the final segment, the paper presents a critical evaluation of computerized transferable skills analysis (TSAs) in the occupational disability context. By contrast, support is offered for the notion that (neuro) psychovocational assessments and situational work assessments should play a key role in "own occupation" disability determination, even where specific vocational rehabilitation/retraining recommendations are not requested by the referral source (e.g., insurer disability examination).
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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 | 0.046 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.011 | 0.042 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".