Construct Validity of the Professional Quality of Life (ProQoL) Scale in a Sample of Child Protection Workers
Bibliographic record
Abstract
The Professional Quality of Life (ProQOL) scale is one of the most widely used measures of compassion satisfaction and fatigue despite there being little publicly available evidence to support its validity. This study, conducted among a sample of 310 child protection workers, assessed the construct validity of this measure using confirmatory factor analysis (CFA) and bifactor modeling. The CFA failed to confirm the adequacy of the three-factor structure proposed by Stamm (2010). In response, a bifactor model postulating a factor structure with a general factor in addition to independent factors (compassion satisfaction, job burnout, and secondary traumatic stress) was proposed, highlighting the unidimensionality of the ProQOL while allowing for each subscale to be used separately. Moreover, this bifactor model of the ProQOL was moderately correlated with the Posttraumatic Disorder Checklist, r = -.427, p < .001, and strongly correlated with scales of well-being at work, r = .694, p < .001, and psychological distress at work, r = -.666, p < .001, thus supporting the ProQOL's convergent validity. No associations were found between the ProQOL and the Life Event Checklist, which supports the ProQOL's discriminant validity. Overall, the results indicated that compassion satisfaction and compassion fatigue represent higher and lower levels of the same construct rather than two different constructs. Researchers and clinicians could therefore compute a single score to rate professionals' individual levels of professional quality of life.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".