Investigating the Relationship Between Self-Confidence and Burnout for Professionals Supporting Adults with Developmental and Intellectual Disabilities Engaging in Challenging Behaviour
Bibliographic record
Abstract
Professionals supporting adults with developmental and intellectual disabilities who engage in challenging behaviour (CB) are at increased risk for burnout, which contributes to the workforce crisis in the developmental services sector. Research on factors influencing burnout for professionals in this field comprises a growing body of literature, but the relationship between self- confidence and burnout remains unclear. This convergent mixed methods study aimed to explore support professionals’ conceptualization of self-confidence and potential factors associated with it and quantify the relationship between self-confidence and burnout. An online survey containing the Maslach Burnout Inventory and a self-confidence instrument, along with other closed- and open-ended questions, was distributed to 156 types of direct support professionals and 46 leaders in developmental services across Ontario, Canada (N=202). A preliminary model of self- confidence was constructed that contains several person-related, social, knowledge, and situational components. Length of time in the field, level of support from others, and types of CB exposed to were acknowledged factors reported to influence self-confidence. Furthermore, increased self- confidence significantly predicted lower burnout scores characterized by decreased emotional exhaustion, R2 = .048, F(1, 146) = 7.29, p = .008, decreased depersonalization, R2 = .026, F(1, 146) = 3.92, p = .049, and increased personal accomplishment, R2 = .097, F(1, 146) = 15.68, p < .001. Results may increase understanding of burnout and reduce burnout risk, thus enhancing the quality of supports provided. Additionally, the model of self-confidence may inform pertinent staff training targets for organizations in this field.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".