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Record W4212891348 · doi:10.46743/2160-3715/2022.3620

Therapeutic Processes in Clinical Interventions : A View of Qualitative Methodological Approaches

2022· article· en· W4212891348 on OpenAlexaff
Jennifer Denis, Marc Tocquet, François Guillemette, Stéphan Hendrick

Bibliographic record

VenueThe Qualitative Report · 2022
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsPsychological interventionIntervention (counseling)Qualitative researchPsychotherapistPsychologyField (mathematics)Session (web analytics)Clinical PracticeEngineering ethicsManagement scienceEpistemologyMedicineSociologyComputer scienceNursingSocial sciencePsychiatry

Abstract

fetched live from OpenAlex

This article examines several qualitative methods to capture and analyze processes in therapeutic and clinical interventions. The study of therapeutic processes provides an understanding of what leads to changes in clinical interventions. This is a goal of any therapeutic intervention. This interest should allow us to try to identify what the therapists do and think they are doing, how they do it, how they think about their interventions, and what happens during the session that might explain changes. These types of studies require that researchers provide clarifications about their epistemological and methodological choices. To meet that requirement, we propose to review a range of issues, methodologies, and tools – which come from qualitative research - that guide us in conducting research in the psychotherapeutic and clinical field. The aim of our article is to put forward a methodological framework for researchers to better explore the patient’s or the therapist’s lived-experience and better reveal, moment-to-moment, the clinical practice.

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)0.025
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.871
GPT teacher head0.708
Teacher spread0.163 · 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.

Study designQualitative
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
Published2022
Admission routes1
Has abstractyes

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