Exploring links between sensory processing sensitivity, professional quality of life, and negative affectivity in clinical and counselling psychologists
Bibliographic record
Abstract
Mental health professionals, like professionals in other fields, experience a high risk of burnout. Situational as well as individual factors can increase this risk. The current study explored possible mediators of the relationships between the personality trait of sensory processing sensitivity (SPS) and the risk of poor professional quality of life and negative affectivity in a group of 95 clinical or counselling psychologists. Participants completed self-report measures of SPS, three proposed mediators (perceived negative impacts of the COVID-19 pandemic, compassion satisfaction, and self-compassion) and five different outcome variables (burnout, secondary traumatic stress, stress, anxiety, and depression). Subscale scores on the measures of SPS were used to compute composite scores reflecting the strength of positive (SPS+) and negative (SPS-) trait clusters. The three proposed mediators were entered into each mediation model in parallel to allow us to tease apart the unique roles they played in explaining links between these two facets of SPS and our outcome variables. Reporting higher levels of SPS- traits predicted greater perceived pandemic burden and lower levels of self-compassion and compassion satisfaction. Links between SPS- traits and increased risk of all five negative outcomes were fully or partially mediated by at least two of the mediators. In contrast, high scores on the SPS+ trait cluster predicted higher levels of compassion satisfaction which predicted lower levels of burnout and secondary traumatic stress. This study identified several important risk and protective factors for clinical and counselling psychologists with SPS. By exploring the nature of the association between SPS and professional quality of life and mental health, the findings may inform proactive measures to support those with SPS who work in this vital area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".