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Record W4390679908 · doi:10.1521/jsyt.2023.42.3.22

Solution-Focused Play Therapy: The Magic of Strengths-Based Play

2023· article· en· W4390679908 on OpenAlexvenueno aff
Elizabeth Kjellstrand Hartwig, Elizabeth R. Taylor

Bibliographic record

VenueJournal of Systemic Therapies · 2023
Typearticle
Languageen
FieldPsychology
TopicChild Therapy and Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionDirectivePsychologyMAGIC (telescope)PsychotherapistCognitive skillCognitionPlay therapyComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Solution-focused play therapy (SFPT) is a strengths-based approach to play therapy that helps children work through challenges by building on internal assets and external resources (Hartwig, 2021). SFPT is comprised of specific play therapy skills and techniques that are developmentally appropriate for all ages of children, with an emphasis on nondirective skills for younger children and more directive skills as children gain cognitive and verbal abilities. As a strengths-based theoretical modality, SFPT focuses on identifying small, measurable goals, and then working with the child to identify skills and strategies that can help them reach their goals. This article will review the history, evidence base, and foundations of SFPT, the solution-building process, and SFPT basic skills, techniques, and interventions. This article serves as an overview of the SFPT approach and underscores why this approach should be recognized in the play therapy field.

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)0.003
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.011
Scholarly communication0.0040.005
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.028
GPT teacher head0.297
Teacher spread0.269 · 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
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

Citations1
Published2023
Admission routes1
Has abstractyes

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