‘So I Feel Like I'm Getting It and Then Sometimes I Think OK, No I'm Not’: Couple and Family Therapists Learning an Evidence‐Based Practice
Bibliographic record
Abstract
This research concerns itself with the experiences of couple and family therapists (CFTs) learning about and using an evidence‐based practice (EBP). The engagement with EBP is growing across many aspects of the mental health and health care systems. The EBP model is now being applied in a broad range of health and human service systems, including mental and behavioural health care, social work, education, and criminal justice (Hunsley, 2007). The dialogue about the role of evidence‐based approaches in the practice of CFT and research literature is also evolving (Sexton et al., 2011; Sprenkle ). Interestingly, while the research delves into what are the best approaches with different populations and presenting issues, little research has explored the experience ofCFTs themselves, particularly while learning an EBP. Using a phenomenological approach called interpretive phenomenological analysis (Smith, Flowers & Larkin, 2009), this research explores the experiences ofCFTs learning and using an EBP. The paper reports on key issues, challenges, and areas forCFTs, educators, and supervisors. As researchers, educators, administrators, policy makers, andCFTs struggle with what works best with which populations and how best to allocate resources, this research contributes to dialogues about how best to educate and supportCFTs, and the complexity of doing research in real‐life settings.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".