Patient experiences of using mental health services in Canada: Scoping review
Bibliographic record
Abstract
Introduction: Research that focuses on patient experiences can provide much needed insight on the strengths and weaknesses of the current mental healthcare system and provide direction for improvement. To give voice to patients, this manuscript focuses on a scoping literature review that was conducted to determine patient experiences of using mental health services in Canada. Methods: Arksey and O’Malley outlined the scoping study methodology that was used for this manuscript. It includes the following five stages: 1) identifying the research question, 2) identifying relevant studies, 3) study selection, 4) charting the data, and 5) collating, summarizing, and reporting the results. Results: The following themes emerged from articles that discussed negative experiences of patients who used mental health services in Canada: 1) discrimination, 2) unmet needs, and 3) invalidation. The following themes emerged from articles that discussed positive experiences of patients who used mental health services in Canada: 1) compassion, 2) validation, and 3) personal autonomy. Conclusion: When patients had negative experiences, it tended to be because they felt discriminated against, their needs were not met, and they felt invalidated. For patients to have positive experiences they need to feel compassion from staff, a sense of personal autonomy, and validation. Sensitivity, empathy, and diversity training could better equip staff to deal with patients with mental illness which could lead to more positive patient experiences of using mental health services. Staff and people with lived experience can educate patients on how to navigate the mental healthcare system to meet their needs which can also lead to more positive patient experiences of using mental health services.
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 | 0.025 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.020 | 0.050 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".