Counselling the (self?) diagnosed client: generative and reflective conversations
Bibliographic record
Abstract
In this paper, we address the phenomenon of clients who present their concerns in the medicalised discourse of the Diagnostic and Statistical Manual of Mental Disorders – Fifth Edition (DSM-5). We contextualise this phenomenon, highlighting how a ‘diagnose-and-treat’ logic increasingly pervades everyday understandings and informs people's efforts to make sense of their concerns. We relate these cultural ways of sense-making to discursive counselling practice, noting possibilities for circumventing ‘discursive capture’ through reflective and generative dialogues. We then turn to two common ways in which clients present their concerns in counselling: (1) arriving self-diagnosed or diagnosed by another professional and (2) as a family in which parents present a child as having a mental disorder. We suggest ways of moving beyond medicalised discourse via resourceful and critically reflective conversations with clients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".