Professional dissonance as a predictor of job dissatisfaction and psychological distress among social work professionals: a cumulative risk model
Bibliographic record
Abstract
In this doctoral dissertation study, the concept of professional dissonance posits that job dissatisfaction and psychological distress can result from the cumulative effect of competing, often contradictory, work demands and role obligations. Social workers, as individuals, professionals, and members of the broader society can experience dissonance resulting from identity traits, value system conflicts, and extracurricular social roles. A conceptual model of professional dissonance is presented, demonstrating potential sources of dissonance across personal-professional, moral-ethical, organizational-structural, and historical-pedagogical domains as they apply to social work theory and practice. To explore this conceptual model, a mixed method but primarily quantitative study was undertaken with a random sample of 261 registered social workers in Ontario, Canada. The cumulative risk model was used as the study framework given its prior usage in the social services and suitability to the research question. In addition to univariate and bivariate analyses, a multivariate model was developed and tested as an explanatory framework for the observed relationships between variables. A negative linear relationship was demonstrated between professional dissonance and job satisfaction and job satisfaction and psychological distress, and a positive relationship was noted between professional dissonance and psychological distress. Informed by these findings, implications for social work practice, career choice, education, and regulation and leadership, as well as recommendations for future inquiry are discussed.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".