‘Get on Board or Get Off’: Nosediving Job Quality for Mental Health Providers in the Age of Platform Work
Bibliographic record
Abstract
This article situates teletherapy platforms within the context of digital labour platforms. It explores commonalities including workers’ employment status, worker autonomy and platform control, user recruitment practices and working time flexibility to show how teletherapy platforms have adopted the platform business model to mental health care. Presenting a first-person account from Nelly, a psychoanalytic psychotherapist and social worker based in a large city in the Northeastern United States, the article reveals how teletherapy platforms erode work quality and increase precarity. While Nelly’s experience ends in a successful unionisation campaign, it also warns of the innate tensions between startup culture and the conditions that foster quality mental health care.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".