Development of the emotionally focused individual therapy adherence measure: Conceptualisation and preliminary reliability
Bibliographic record
Abstract
Abstract The Emotionally Focused Individual Therapy Adherence Measure (EFIT‐AM) is presented as a scale to measure a therapist's adherence to the EFIT model. The theoretical rationale for EFIT as a promising model of individual therapy and conceptual development of EFIT‐AM are introduced. The EFIT‐AM was developed to measure therapist adherence to treatment tasks when working specifically with clients presenting with negative emotional disorder and can be used to promote therapist education and development in training and supervision. The measure includes assessment of essential skills, meta‐themes, and stages of EFIT. The measure was piloted using participants (n = 20) with advanced training in EFT. Participants used the measure to rate therapist adherence to EFIT model by observing a recorded therapy session of an expert EFIT therapist. Participant ratings were used to examine consistency among ratings of therapist behaviour and to receive feedback regarding the user experience of the EFIT‐AM. Mean item ratings of three and five within the same talk turn were considered to signify reliable identification of an EFIT skill. Of the 18 adherence items on the EFIT‐AM, 12 items met our criteria for 50% of participants identifying the item at the same time with a rating of three or five. Six items did not meet these criteria and were considered to either occur at a session, as opposed to talk turn, level or in need of consolidation. The high level of inter‐rater reliability and internal consistency of the EFIT‐AM indicates the EFIT‐AM is a promising tool to evaluate therapist adherence.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".