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Record W4313157384 · doi:10.1521/jsyt.2022.41.2.1

Collaborative Documentation: Therapist Experiences in Jointly Writing Progress Notes

2022· article· en· W4313157384 on OpenAlexvenueno aff
Michael D. Reiter, Vanessa Bibliowicz, Kayleigh Sabo, Xinyan Cindy Yu, Yesenia Delgado, Desiree Barrionuevo, Bailey Rich

Bibliographic record

VenueJournal of Systemic Therapies · 2022
Typearticle
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsDocumentationSession (web analytics)Privilege (computing)Biopsychosocial modelPsychologyPsychotherapistProcess (computing)Medical educationComputer scienceMedicineWorld Wide Web

Abstract

fetched live from OpenAlex

Systemic therapy is predicated on a collaborative relationship between therapist and client. This joint pursuit of client goals occurs in the therapy room but may dissolve once the therapist begins filling out any necessary paperwork (e.g., progress notes, biopsychosocial evaluations, or assessments). Collaborative documentation is one means of bringing forth the client's voice during the session and for documentation. Most therapists write progress notes on their own once the session has ended; however, this leads to a privileging of the therapist's voice in the document rather than the client's voice. This article explores collaborative documentation and provides the voices of doctoral student-therapists as they experienced their initial forays into this process. We provide an explanation of how we believe collaborative documentation helped privilege the client's voice, decreased the power imbalance between therapist and client, and provided ideas as to the implementation and use of joint progress note development.

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)0.033
metaresearch head score (Gemma)0.095
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.095
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0180.015
Scholarly communication0.0110.011
Open science0.0040.021
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0050.001

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 head0.016
GPT teacher head0.314
Teacher spread0.299 · 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 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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