The Use of Personal Projects Analysis to Enhance Occupational Therapy Goal Identification
Bibliographic record
Abstract
Background: Client-centered occupational therapy begins with the identification of personally-relevant patient goals. This study aimed to determine whether the elicitation module of Personal Projects Analysis (PPA) could help patients in an acquired brain injury day hospital program identify more meaningful goals than those identified using the Canadian Occupational Performance Measure (COPM) alone. Method: Ten patients completed the COPM. They rated the importance of each goal and their confidence that they could attain each goal. During the next session, using the elicitation module of PPA, they identified personal projects just prior to their brain injuries, current personal projects, and future desired personal projects. They were then invited to revise their COPM goals and re-rate them for importance and confidence. Results: Following completion of the elicitation module of PPA, seven participants changed at least one goal. Of the goals that were changed, half were revised to include the mention of another person. There were no significant changes in average goal importance or perceived attainability. Occupational therapists reported that the elicitation module of PPA helped them get to know their patients better and identify potential therapeutic occupations. Discussion: The elicitation module of PPA may help people develop goals that are more embedded in their social contexts.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".