Using intervention mapping to deconstruct cognitive work hardening: a return-to-work intervention for people with depression
Bibliographic record
Abstract
BACKGROUND: Mental health related work disability leaves are increasing at alarming rates with depression emerging as the most common mental disorder in the workforce. Treatments are available to alleviate depressive symptoms and associated functional impacts; however, they are not specifically aimed at preparing people to return to work. Cognitive work hardening (CWH) is a novel intervention that addresses this gap in the health care system. This paper presents a theoretical analysis of the components and underlying mechanisms of CWH using Intervention Mapping (IM) as a tool to deconstruct its elements. METHODS: The cognitive sequelae of depression and their relevance to return-to-work (RTW) are examined together with interpersonal skills and other work-related competencies that affect work ability. IM, a tool typically used to create programs, is used to deconstruct an existing program, namely CWH, into its component parts and link them to theories and models in the literature. RESULTS: CWH has been deconstructed into intervention elements which are linked to program performance objectives through underlying theoretical models. In this way, linkages are made between tools and materials of the intervention and the overall program objective of 'successful RTW for people with depression'. An empirical study of the efficacy of CWH is currently underway which should provide added insight and understanding into this intervention. CONCLUSIONS: The application of IM to CWH illustrates the theoretical underpinnings of the treatment intervention and assists with better understanding the linkage between intervention elements and intervention objective. Applying IM to deconstruct an existing program (rather than create a program) presents an alternate application of the IM tool which can have implications for other programs in terms of enhancing understanding, grounding in theoretical foundations, communicating program design, and establishing a basis for program evaluation and improvement.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".