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Record W614002363

Differences in the Utilization of Therapeutic Use of Self by Occupational Therapists in Military and Civilian Settings

2010· article· en· W614002363 on OpenAlexvenueno aff
Gill Cj

Bibliographic record

VenueSound Ideas (University of Puget Sound) · 2010
Typearticle
Languageen
FieldPsychology
TopicCounseling Practices and Supervision
Canadian institutionsnot available
Fundersnot available
KeywordsMilitary personnelMedicinePsychologyPsychotherapist
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the implementation of therapeutic use of self, and the factors that may influence that implementation in military and civilian settings, as described by occupational therapists who have experience in both settings. A semi-structured qualitative design was used to interview two practicing occupational therapists. Analysis of the audio transcripts resulted in two themes on the comparison of implementation of therapeutic use of self in military and civilian settings: Knowing your Population (identifying differences between the military and civilian settings) and Some Things Do Not Change (identifying similarities between the military and civilian settings). Factors influencing the implementation of therapeutic use of self in the military setting included the themes of The Military Medical System, The Military Structure and Purpose, and The Importance of Intimately Knowing about the Military as a Military Practitioner. Many of the underlying concepts of therapeutic use of self agreed with previous literature and theoretical concepts regarding therapeutic use of self. This was the first study investigating differences between military and civilian settings. Implications of this study are that a therapist should know his or her client base, be prepared to employ many means of creating rapport and promoting “buy-in,” and become familiar with the client population language or jargon. The military as a community unto itself has a distinct language, jargon, and culture that influence the implementation of therapeutic use of self.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.045
GPT teacher head0.291
Teacher spread0.246 · 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 designObservational
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
Published2010
Admission routes1
Has abstractyes

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