Assessing Work-Related Stressors in Online Counseling: Cross-Sectional Questionnaire Development Study
Bibliographic record
Abstract
Background: The rapid digitalization of health and social services, particularly accelerated by the COVID-19 pandemic, has led to a widespread adoption of online counseling. While offering flexibility and improved access for clients, online counseling presents new challenges for counselors, including technical issues, difficulties in building therapeutic relationships, and changes in work organization. Despite growing reliance on digital counseling platforms, there is a lack of validated tools to assess specific occupational stressors associated with online counseling. Objective: This study aimed to develop and evaluate the "QueStrOn" (Questionnaire to Assess Stressors in Online Counseling), an instrument designed to identify stressors and resources specific to online counseling and to explore its ability to predict perceived stress levels in counselors. Methods: Item development was guided by the Job Demands-Resources model, qualitative interviews with 22 counselors, expert input, and a literature review. A preliminary version of the questionnaire was pretested and then distributed via email and social media to counselors offering both online and face-to-face services. A total of 219 counselors completed the survey, and after applying inclusion criteria, 174 responses were analyzed. Exploratory factor analysis was conducted using principal axis factoring and varimax rotation. Internal consistency was assessed via Cronbach alpha (α). A linear regression model was used to initially test the predictive power of the identified factors with perceived digital stress as the dependent variable. Results: The exploratory factor analysis resulted in a four-factor solution with 16 items, capturing (1) Online Work Organization, (2) Online Framework, (3) Online Work Content, and (4) Online Communication. The overall instrument demonstrated high internal consistency (α=0.870), with acceptable values for factors (1), (3), and (4) (α=0.754, 0.745, and 0.826, respectively), although the factor "Online Framework" showed limited reliability (α=0.502). The regression model, adjusted for age and gender, significantly predicted perceived stress in online counseling (F5=13.335, P<.001), explaining 27.1% of the variance. Online Work Organization, Online Communication, and Online Framework were associated with lower perceived stress when rated positively, whereas Online Work Content showed an inverse relationship, potentially reflecting emotional distancing. Conclusions: The QueStrOn instrument provides a valid first step toward systematically assessing occupational stressors in online counseling. Its 4-factor structure aligns with theoretical and empirical findings and offers practical utility for workplace risk assessments. Incorporating these dimensions into routine evaluations may support counselor well-being and inform digital health policy. Further validation and longitudinal studies are recommended to expand its applicability and explore associations with broader health outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".