Depression on the frontline: an examination of the impact of working conditions and life stressors on sex workers, stylists and servers
Bibliographic record
Abstract
Changes to the global economy over the past few decades along with growing support for neo-liberal policies in Canada have led to an increase in precarious, low-wage frontline service work. These kinds of occupations often involve sustained interaction with clients and have high job demands, low job control and insufficient monetary reward. Further, many of these jobs also tend to be gendered (i.e., they involve a large degree of ‘emotional’ labour or care work that is predominantly carried out by female workers). Working conditions such as these can have a negative impact on the mental health of frontline service workers leading to psychological distress and depression. Chronic stress or cumulative stressful life events can also increase vulnerability to depression. While these stressors can be exacerbated by poor working conditions, they can also exist independently of them. Comparative research across two or more frontline service occupations, similar in broad strokes but differing in workplace characteristics, is especially needed to understand how structural and contextual factors in the workplace and over the life course interact to produce depression. This thesis presents data from my supervisor (Dr. Cecilia Benoit) and colleagues’ 4-wave longitudinal study entitled “Interactive service workers’ occupational health and safety and access to health services” (Benoit, Jansson, Leadbeater & McCarthy, 2005). This is a study of three types of frontline service jobs – two in the formal economy (hairstyling and food and beverage service) and one in the shadow/informal economy (sex industry). Results of this secondary analysis demonstrate that not only do working conditions have a significant impact on the mental health of frontline service workers but that stressful life events also have very strong explanatory power in understanding why certain workers experience depression more than others. The findings indicate that sex workers have the highest levels of depression, in comparison to stylists and servers. Yet sex workers report protective factors in their jobs, including higher comparative decision latitude, that contradict much of the current literature on sex work. The thesis concludes with policy recommendations and gives direction for further research in the area of frontline service work and depression.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".