Development and Pre-Testing of a Rehabilitation Planning Consultation for Head-and-Neck Cancer
Bibliographic record
Abstract
BACKGROUND: In contrast with other major chronic conditions such as heart disease and stroke, cancer care does not routinely integrate evidence-based rehabilitation services within the standard continuum. The objectives of the present project were to develop a rehabilitation planning consultation (rpc) for survivors of head-and-neck (hn) cancer, to test its feasibility, and to make refinements. METHODS: Using intervention mapping, the rpc-alpha was developed by examining potential theoretical methods and practical applications relative to the program objectives. During feasibility testing, a single case series was conducted with survivors of hn cancer who had completed their cancer treatment within the preceding 11 months; iterative refinements were made after each case. RESULTS: The rpc-alpha was led by a rehabilitation professional and was based on self-management principles. The initial consultation included instruction in a global cognitive strategy, goal-setting, introduction to available resources, action planning, and coping planning. A follow-up consultation was conducted a few weeks later. Of 9 participants recruited, 5 completed post-intervention assessments. Participants reported that the rpc helped them to make rehabilitation plans. CONCLUSIONS: The rpc was feasible to use and satisfactory to a small group of hn cancer survivors. A pilot test of the refined version is in process.
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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 | 0.025 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".