In Search of Flow in Counseling and Psychotherapy: Identifying the necessary ingredients of peak moments of therapy interaction / Auf der Suche nach<i>Flow</i>in Beratung und Psychotherapie: Identifikation der notwendigen Bestandteile von Gipfelmomenten therapeutischer Interaktion / En búsqueda de fluidez en counseling y psicoterapia: Identificando los ingredientes necesarios de los momentos pico de interacción en terapia / La recherche de mouvement dans le counseling et la psychothérapie: L'identification des composants nécessaires aux “temps forts” dans l'interaction psychothérapeutique / Em busca do Fluir em<i>Counselling</i>e Psicoterapia: Identificação dos ingredientes dos momentos chave na interacção terapêutica /
Bibliographic record
Abstract
Flow is a key concept in the study of peak moments of human experience and performance (Csikszentmihalyi, 1975, 1988, 2000, 2003). During moments of flow, creativity is enhanced, meaning is created, and well-being grows. Flow has been investigated mainly in business, sports, and creative activities. The aim of this paper is to review the ingredients and conditions of flow and identify the necessary aspects of a flow experience in therapy. Based on a comprehensive review of the flow literature and examination of its relevance to counseling (especially during highly influential, positive moments of therapy), we considered the following five elements as important in capturing the essence of flow during peak moments of therapy: experience of bonding and connectedness, intense concentration on a challenging task/topic, immediate/ongoing feedback, altered sense of time, and growth promotion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".