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Record W2316204204 · doi:10.5539/res.v8n2p133

Heart Drawing: A New Diagnostic Tool

2016· article· en· W2316204204 on OpenAlexvenueno aff
Arthur Becker‐Weidman

Bibliographic record

VenueReview of European Studies · 2016
Typearticle
Languageen
FieldPsychology
TopicChild Therapy and Development
Canadian institutionsnot available
Fundersnot available
KeywordsFeelingAffect (linguistics)PsychologyClass (philosophy)Developmental psychologySocial psychologyCommunicationArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

<p class="Standard">This paper presents a methodology for assessing a child’s capacity to identify primary affective states, affect regulation, and affective experiences in a non-threatening manner. The methodology can be used with children from ages three years thru age nineteen years.</p><p class="Standard">Background</p><p class="Standard">A thorough assessment includes an evaluation of a person’s capacity to identify and regulate emotions. Affect regulation requires the capacity to identify internal experiences of emotions. The Heart Drawing was developed as a non-threatening method for assessing a child’s capacity to identify emotions. Most children enjoy drawing and the Heart Drawing is usually experienced by the child as non-threatening and enjoyable.</p><p class="Standard">The Heart Drawing is a new, easy to use, and efficient tool that allows the clinician to assess a child’s affect regulation functioning, affective range, and experience in a non-threatening manner. It can also be used to assess a child’s insightfulness and capacity to identify internal affective experiences.</p><p class="Standard">Method</p><p class="Standard">The child is asked to select colors for the feelings expressive of mad, sad, glad, and scared from a group of nine primary colors. The child is then asked to draw a heart and to fill in the heart with the amount of each feeling that the child usually feels.</p><p class="Standard">Results</p><p class="Standard">Administration and discussion usually takes ten to fifteen minutes.</p><p class="Standard">Conclusion</p><p class="Standard">The article presents examples of drawings by children with various diagnoses and conditions along with a normative drawing for comparison. The methodology has been found to be very helpful in assessing a child’s emotional status and capacity to regulate emotions.</p><p>Key Practitioner Message</p><p>1) Emotional regulation and the capacity to identify emotions is important for evaluation and treatment.</p><p>2) Projective drawing methods can be useful in assessing a person’s ability to identify and regulate emotions.</p><p>3) The Heart Drawing is an efficient and effective method for assessing a person’s capacity to identify and regulate emotions.</p>

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)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0240.006

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 head0.065
GPT teacher head0.367
Teacher spread0.302 · 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 designBench or experimental
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

Citations6
Published2016
Admission routes1
Has abstractyes

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