Evaluating Clinical Experiences Implementing the Montreal Model for Ketamine Assisted Psychotherapy Adapted for Esketamine
Bibliographic record
Abstract
Background: Burnout among mental health providers is linked to decreased job satisfaction, higher rates of depression and anxiety, lower quality of care, and increased turnover. The Montreal Model for Ketamine-Assisted Psychotherapy, adapted for Esketamine (MMKAPE), was introduced to improve clinician satisfaction by promoting meaningful work, patient-centered care, and interdisciplinary collaboration. This study assesses the impact of MMKAPE on clinician satisfaction and identifies key barriers and facilitators during implementation. Method: A convenience sample of six clinicians (two nurse practitioners, three therapists, and one registered nurse) participated in MMKAPE’s implementation for patients with treatment-resistant depression in an outpatient setting. Monthly surveys measured satisfaction across three domains. Descriptive statistics were used to track trends from baseline to final assessments. Interprofessional meetings and open-ended responses were analyzed to extract themes, with ChatGPT aiding in theme validation and bias reduction. Results: Clinician satisfaction improved in all measured areas. Meaningfulness in work increased from M = 4.3 to M = 4.5, with all clinicians reporting higher engagement. Patient-centered care rose from M = 4.05 to M = 4.17, and interdisciplinary collaboration improved from M = 3.35 to M = 4.17. Nurse practitioners showed the largest gains across all domains, while therapists experienced a slight decrease in work meaning. Key barriers included therapist reimbursement issues and workload strain, while structured team meetings and shared treatment goals emerged as facilitators. Discussion: MMKAPE improved clinician satisfaction and collaboration but was challenged by systemic constraints. Further research should address sustainable funding, long-term outcomes, and broader scalability.
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 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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".