Practice 14 Relational Responsivity: Centring Client Worldviews
Bibliographic record
Abstract
This chapter explores relational responsivity as a means of centering client culture, worldview, and preferences in collaborative healing relationships. By applying a Wise Practices lens to the co-construction of wholistic and culturally responsive counselling processes, practitioners are invited to create therapeutic spaces that honour diverse healing traditions and open further possibilities for integrating and adapting mainstream eurowestern practices through relational–ethical engagement. Drawing on contemporary literature about common factors and the centrality of responsive relationships, the authors examine the synergy between Wise Practices and emergent scholarship in counselling and psychology. Drawing on the practice of Etuaptmumk, also known as Two-Eyed Seeing, they position Wise Practices alongside conventional “best practices” as equally valid considerations in therapeutic decision-making. They offer a model of relational responsivity in practice that balances (a) traditional, cultural ways of knowing and client views of health and healing with (b) conventional psychological approaches and practitioner perspectives. Relational responsivity and Wise Practices are threaded through the themes of honouring client lived experience through cultural inquiry, co-creating cultural hypotheses using meaning-making and antipathologizing perspectives, way-making through conceptualizing change and setting therapeutic directions, and co-constructing change at multiple levels. A Wise Practices framework for therapeutic decision-making is proposed that offers a spectrum of choices ranging from culture-centred approaches to integration to suitable adaptation to eurowestern frameworks, all grounded in client cultural context and views of health and healing. The chapter concludes with an invitation to consider a Wise Practices lens as a foundation for ethical decision-making. Melissa and Sandra appreciate the stand-alone contributions of the following co-authors: ● Ivana Djuraskovic shares a refugee story as a foundation for offering guidance on applying an anti-pathologizing lens to conceptualizing diverse refugee lived experiences. ● Don Zeman shares stories to illustrate his approach to engaging in therapeutic conversations about sexuality with queer clients.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.055 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".