Teaching Counselling Amid the Evolving Evidentiary Landscape
Bibliographic record
Abstract
Historically, professional counselling has been mired by “theory wars” involving proponents going to great lengths to prove (either through research or through rhetoric) that their approach was superior to others. This, not surprisingly, led to rancour and division within professional counselling and a form of camp mentality among model adherents. This paper offers an innovative approach to teaching models of counselling that counters such tendencies. The impetus for this approach stems from a robust and growing body of research indicating that counselling models, although often revered among model adherents and assumed to be the sine qua non of effective counselling, now appear to play a smaller role within the therapeutic enterprise. While few (including the present authors) would argue that counselling models are unnecessary, the various lines of research outlined in this paper compel counsellor educators to rethink how counselling models ought to be taught to graduate-level counselling students.
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 | 0.027 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.059 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".