From Framework to Practice: Person‐Directed Planning in the Real World
Bibliographic record
Abstract
BACKGROUND: Person-directed planning (PDP) is an approach to planning supports that aims to redistribute power from the service system to individuals with intellectual and developmental disabilities (IDD) and natural supports, improve relationships and build community. To do this, the right people with the right attitudes engaging in the right actions are needed. This paper examines how key elements in PDP contribute to successes in planning. MATERIALS/METHODS: Researchers worked with three planning teams from different community service agencies using participatory action research techniques (i.e. free list and pile sort, Socratic wheel, whys/hows exercise). RESULTS: Most key elements of PDP were relevant to each team. Perceptions of which had most contributed to planning successes differed. CONCLUSIONS: The various elements of PDP are used by and useful to planning teams, although some may be more relevant to some successes than others because of specific goals, or the person's strengths and needs.
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 | 0.067 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.012 | 0.063 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.006 | 0.008 |
| 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".