Pourquoi ai-je quitté? : Autoethnographie d'une intervenante de soutien direct en trouble grave du comportement (TGC)
Bibliographic record
Abstract
Cet article propose une autoethnographie sur mon expérience en tant qu’intervenante de soutien direct auprès de personnes en situation de trouble grave du comportement (TGC). À travers l’analyse de mon parcours, j’examine les facteurs systémiques et organisationnels ayant mené à mon départ, ainsi que ceux ayant soutenu mon engagement. L’exposition répétée à la violence, la surcharge émotionnelle et professionnelle, le manque de soutien et l’absence de reconnaissance ont fragilisé ma trajectoire professionnelle. À l’inverse, la relation avec les usager⋅ères et l’appui inspirant de certaines gestionnaires ont été des sources de sens et de motivation. Notre analyse repose sur une reconstruction rétrospective de mon parcours, tout en s’articulant autour du modèle écosystémique (Bronfenbrenner & Cole, 1979) et du concept de bien-être psychologique au travail (Dagenais-Desmarais, 2010). Ce récit met en lumière les tensions entre la santé psychologique au travail et les dynamiques organisationnelles. En prenant appui sur mon expérience, cette réflexion vise à ouvrir un dialogue sur le bien-être des intervenants en soutien direct en TGC et sur la nécessité de transformations structurelles pour prévenir l’épuisement et améliorer la rétention du personnel. English version This article presents an autoethnography on my experience as a direct support professional working with individuals in situations of severe challenging behavior (SCB). Through an analysis of my professional journey, I examine the systemic and organizational factors that led to my departure, as well as those that sustained my commitment. Repeated exposure to violence, emotional and professional overload, lack of support, and the absence of recognition undermined my professional path. In contrast, my relationships with clients and the inspiring support of certain managers were sources of meaning and motivation. Our analysis is based on a retrospective reconstruction of my professional trajectory, grounded in the eco-systemic model and the concept of psychological well-being at work (Dagenais-Desmarais & Privé, 2010). The narrative highlights the tensions between psychological health in the workplace and organizational dynamics. Drawing on my experience as a direct support professional, this reflection seeks to open a dialogue about the well-being of workers in SCB settings and on the need for structural changes to prevent burnout and improve staff retention.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".