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Record W2163127183 · doi:10.3109/01942638.2013.794187

Addressing the Challenges of Collaborative Goal Setting with Children and Their Families

2013· review· en· W2163127183 on OpenAlexaff
Kelly Brewer, Nancy Pollock, F. Virginia Wright

Bibliographic record

VenuePhysical & Occupational Therapy In Pediatrics · 2013
Typereview
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsMcMaster UniversityHolland Bloorview Kids Rehabilitation Hospital
Fundersnot available
KeywordsGoal settingRehabilitationIntervention (counseling)Process (computing)Goal orientationPsychologyComponent (thermodynamics)Process managementApplied psychologyPsychotherapistComputer scienceSocial psychologyBusinessPsychiatry

Abstract

fetched live from OpenAlex

Collaborative goal setting between clinicians and clients/families is considered a fundamental component of the pediatric rehabilitation process. However, truly client-centered goal setting is not without its challenges. The purpose of this paper is to highlight theoretical concepts relevant to rehabilitation goal setting, review clinical studies directly evaluating relationships between goal setting and pediatric rehabilitation outcomes, and provide recommendations to facilitate collaborative goal processes. Four theoretical frameworks were identified that may lie behind and help explain the effectiveness of collaborative goal setting. The four relevant outcome studies found in the review revealed that individualized goal setting is an important component of the intervention, engages families more actively in therapy, and is associated to some extent with positive outcomes. The evidence suggests that the impact of fully collaborative goal setting is sufficiently positive to support investment of organizational and individual time, energy, and resources to make it an integral part of the rehabilitation process.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0040.005
Open science0.0030.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.175
GPT teacher head0.446
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations104
Published2013
Admission routes1
Has abstractyes

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