Bibliographic record
Abstract
Since children are generally not good candidates for “talk therapy”, play is often a modality that therapists use to enhance understanding and communication in treatment. Among many possible assessment tools suggested in work with children are puppets which appeal to a wide age range and are familiar toys to many. This article describes and compares nine different puppet therapy assessment techniques. The assessment techniques are: Spontaneous Group Puppetry; The Puppet Therapy Assessment Technique; The Family Puppet Interview; The Ross Family Puppet Technique; Puppets in Psychotherapy Scales; The Dynamics of Three; The Puppet Sentence Completion Task; The Berkeley Puppet Interview; and The Affect in Play Scale. The current study is a qualitative interview study in which thematic analysis has been applied. Five themes emerged in the analysis: methods and techniques in puppet assessment; assessment in therapy: assessment in diagnosis; different levels of structure; and what works for whom. The level of structure is emphasized in the discussion, as are age considerations. Conclusions suggest aspects in favor of, and against, using these assessments. Aspects in favor of using the techniques are, for example, the need for therapist flexibility; the necessity to create rapport; the importance of an appropriate assessment tool; and the requirement to adapt the level of structure to the client. Possible contraindications may be the child’s age, because young children may be too immature for symbolic play, and severely disturbed children may lack the necessary emotional and/or cognitive skills for puppet play.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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 teacher head, 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".