Roots of Care: What Drives Clinicians to Serve Diverse Clients
Bibliographic record
Abstract
Purpose - The purpose of this paper is to examine the perspectives and motivations of mental health clinicians working with immigrant and socio-culturally diverse populations. Specifically, it explores the pathways that lead professionals to engage in this work, the values underpinning their career choices, and how their motivations influence their practice. Design/methodology/approach - This study employed a qualitative descriptive design to examine clinicians' perspectives. Semi-structured interviews were conducted with nineteen mental health clinicians working in Canadian community-based organizations that serve equity-deserving groups. Participants were recruited using purposive and snowball sampling. Interviews explored clinicians’ backgrounds, motivations, and approaches to mental health care. Data were analyzed using inductive thematic analysis, allowing for the identification of key themes that capture participants’ experiences. Findings - four themes emerged from the data and are presented: 1) Personal background; 2) diverse needs and mental health awareness; 3) reduce stigma and stereotypes; and 4) current equity imitates. The findings highlight how participants’ personal and professional experiences are deeply intertwined with their motivations to address systemic inequities. This, in turn, translates into their clinical practices, where they prioritize cultural humility, cultural responsiveness and the co-creation of safe therapeutic spaces. Originality/value - By contributing to a nuanced understanding of these dynamics, this research aims to highlight the need for greater diversity amongst the mental health care system and promote greater inclusive and equitable services. It highlights the transformative potential of integrating cultural humility and intersectionality into mental health care. Keywords: Immigrant, mental health, clinicians, motivations, diverse population, cultural responsiveness
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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".