Using Multivariate Concept-Mapping for Examining Client Understandings of Counselling
Bibliographic record
Abstract
Counsellors rely on sound research to inform their practice. However, many methods in counselling research that seek to categorize clients’ experiences (including the Critical Incident Technique) inadvertently examine the client’s perspective through the lens of investigator-derived or counsellor-derived constructs and categories – thereby blurring counsellor and client perspectives. Multivariate concept-mapping (MVCM) is a mixed methods research design that ensures the client’s perspective is a more fitting representation of the client’s subjective experience of counselling processes. This article introduces MVCM as an analytical method for understanding the clients’ perspective on counselling and provides a guide for conducting MVCM research on counselling processes. It is hoped that this article will result in greater awareness of this research method amongst counsellor educators and counselling researchers in Canada, resulting in a better understanding of the client’s experiences of counselling processes, which should inevitably benefit counselling practitioners. In the meantime, this article can serve as an impetus for counsellors to carefully scrutinize the categorization-based research on the client’s perspective that they use to inform their practice.
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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 | 0.047 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".