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Record W2203826338 · doi:10.15453/2168-6408.1186

The Use of Personal Projects Analysis to Enhance Occupational Therapy Goal Identification

2016· article· en· W2203826338 on OpenAlexaffabout
Mary Egan, Lori Scott-Lowery, Cynthia De Serres Larose, Liane Gallant, Chantal Canada Jaillet

Bibliographic record

VenueThe Open Journal of Occupational Therapy · 2016
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsOccupational therapyIdentification (biology)Goal settingPsychologyMedicinePhysical therapySocial psychology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.464
GPT teacher head0.573
Teacher spread0.109 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2016
Admission routes2
Has abstractyes

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