Neoliberalism and Mental Health Care in Ontario: A Critique of Internet-Based Cognitive Behavioural Therapy
Bibliographic record
Abstract
In this paper, I offer a critical analysis of Ontario’s mental health strategy, “Roadmap to Wellness,” and the government’s investment in internet-based cognitive behavioural therapy (iCBT) programs as a frontline strategy to address the province’s mental health crisis. Though I acknowledge that publicly funded mental health care is a step in the right direction, I argue that the choice to provide cognitive behavioural therapy (CBT) as the only form of publicly funded therapy indicates a problematic commitment to the maintenance of neoliberal governance. To argue this point, I use discourse analysis to explore the language present in Ontario’s two current iCBT programs – AbilitiCBT and Mindbeacon – and demonstrate the ways in which they reinforce a neoliberal discourse of mental health by emphasizing the values of 1) individual responsibility, 2) productivity, and 3) recovery. More broadly, I argue that neoliberal forms of governance ultimately produce the mental health crises that they seek to address through neglecting the social determinants of health and defunding of social services and assert that critiques of mental health care must address the socioeconomic conditions within which they are implemented, given the intimate relationship between neoliberalism, managerialism, and public policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.032 | 0.106 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| 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".