MétaCan
Menu
Back to cohort
Record W2757165686 · doi:10.1111/jppi.12209

Impact of an Individualized Planning Approach on Personal Outcomes and Supports for Persons With Intellectual Disabilities

2017· article· en· W2757165686 on OpenAlexaff
Dorothy M. Griffiths, Frances Owen, Maurice A. Feldman

Bibliographic record

VenueJournal of Policy and Practice in Intellectual Disabilities · 2017
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsBrock UniversityConestoga College
FundersAdministration for Community Living
KeywordsPsychologyIntellectual disabilityAdvance care planningPromotion (chess)Quality of life (healthcare)Process managementMedicineNursingBusinessPsychiatryPsychotherapistPolitical science

Abstract

fetched live from OpenAlex

Abstract Planning initiatives for individuals with intellectual disabilities (ID) have shifted from traditional planning primarily conducted by caregivers to an individualized planning approach controlled by the person with ID him/herself. The goal of this paradigm shift is to increase individualization of supports to accomplish personal objectives and improve quality of life. Despite the widespread acceptance and promotion of individualized planning, there has been little empirical research to demonstrate its effectiveness. This study compares traditional planning to individualized planning on supports obtained and personal objectives accomplished using a randomized between‐group design. Persons receiving an individualized planning process improved in both supports and personal outcomes as compared to the traditional planning group. When the traditional planning group subsequently received individualized planning, they replicated the results of the first individualized planning group. The findings support implementation of an individualized planning approach in service agencies for individuals with ID.

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)0.003
metaresearch head score (Gemma)0.222
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.222
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.139
GPT teacher head0.477
Teacher spread0.338 · 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 designQualitative
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

Citations11
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Policy and Practice in Intellectual DisabilitiesSame topicFamily and Disability Support ResearchFrench-language works237,207