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Record W3008367582 · doi:10.1002/jclp.22942

Psychotherapy for personal growth? A multicultural and multitheoretical exploration

2020· article· en· W3008367582 on OpenAlexaboutno aff
Katie Aafjes‐van Doorn, Cristián Javier Garay, Ignacio Etchebarne, Céline Kamsteeg, Andrés Roussos

Bibliographic record

VenueJournal of Clinical Psychology · 2020
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPsychotherapistPersonal developmentModalitiesMental healthAutonomyHumanistic psychologyHumanism

Abstract

fetched live from OpenAlex

OBJECTIVE: This paper reports on a focus-group discussion of four expert psychotherapy researchers facilitated at an international conference from the Society of Psychotherapy Research. A discussion was facilitated to explore their perspectives on psychotherapy practices of personal growth (intentionally working towards a strengthened sense of autonomy, mastery, and self-acceptance) in different countries (United States, Canada, Argentina, and Chile) and different modalities (psychoanalysis, humanistic therapy, and cognitive behavioral therapy). METHODS: Following the conference, the audio recording of this discussion was transcribed and analyzed using consensual qualitative research methods. RESULTS: Six domains were identified; definition of personal growth, mental health care systems, psychotherapy practice, psychotherapy research, client and therapist characteristics, and social stigma. RESULTS: Six domains were identified; definition of personal growth, mental health care systems, psychotherapy practice, psychotherapy research, client and therapist characteristics, and social stigma. CONCLUSION: Future research examining the cost-effectiveness and benefits of psychotherapy for personal growth is warranted. Building on the six domains, specific future research projects on the evidence-based practice of psychotherapy for personal growth are suggested.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.290
GPT teacher head0.585
Teacher spread0.296 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations3
Published2020
Admission routes1
Has abstractyes

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