Social Representation of Social Work in the Canadian Healthcare Setting: Negotiating a Professional Identity
Bibliographic record
Abstract
Abstract Our article concerns the social representation of social work held by professional social workers in a Canadian healthcare setting. While there have been extensive theoretical debates on the professional identity of social workers, little is known about the representation of social work held by practitioners themselves. To better understand the viewpoints of practitioners, we performed an integrated content analysis of 30 semi-directed interviews focussing on the question of professional identity with clinical social workers practicing in three Canadian cities. The analysis identified four elements organising the social representation of social work: ‘counselling for support’, ‘empowerment and respect’, ‘social justice’ and ‘compassionate vocation’. Each element has implications for the daily practice of the profession, the cultivation of resilience and the core values of social work. This article further discusses the respondents’ perception of outside views of the profession and explores the tensions between the values of social work and its implementation in an institutional setting. This research seeks to open new lines of inquiry about social work based on the experiences and points of view of front-line practitioners. In proposing our representational analysis of the professional identity of social workers, we hope to further the understanding of social work in general.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.047 | 0.047 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".