How Clients Benefit from Psychotherapy: An Exploration of Unanticipated Positive Outcomes
Bibliographic record
Abstract
Psychotherapy has been found to be highly effective, and yet we are still learning why. We do not know much about what is significant for clients about their therapy experiences, or how they may benefit from therapy in unexpected ways. Using an interpretive phenomenological analysis methodology, this investigation sought to answer the question, “What are clients’ experiences of benefitting from therapy in unanticipated ways?” Six participants were recruited through purposive sampling from a counselling centre in Edmonton, Alberta. Participants were interviewed individually using semi-structured, open-ended questions that served to explore the phenomenon of experiencing unanticipated outcomes from counselling, and the significance of these experiences. Four main themes emerged from participant descriptions that encompassed this phenomenological experience including: (1) having a supportive therapeutic relationship; (2) growing; (3) engaging more in life; and (4) going beyond the problem. The results are discussed in terms of both psychotherapy processes and outcomes. They also serve to help us understand positive changes that can result from psychotherapy, beyond those targeted within sessions. As the first study to explore this phenomenon, the results are useful for better understanding how clients make use of therapy and provide avenues for future research.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".