Perceptions on barriers, facilitators, and recommendations related to mental health service delivery during the COVID-19 pandemic in Quebec, Canada: a qualitative descriptive study
Bibliographic record
Abstract
Abstract Background There was an increase in self-reported mental health needs during the COVID-19 pandemic in Canada, with research showing reduced access to mental health services in comparison to pre-pandemic levels. This paper explores 1) barriers and facilitating factors associated with mental health service delivery via primary care settings during the first two pandemic waves in Quebec, Canada, and 2) recommendations to addressing these barriers. Methods A qualitative descriptive study design was used. Semi-structured interviews with 20 participants (health managers, family physicians, mental health clinicians) were conducted and coded using a thematic analysis approach. Results Barriers and facilitating factors were organized according to Chaudoir et al. (2013)‘s framework of structural, organizational, provider- and patient-related, as well as innovation (technological modalities for service delivery) categories. Barriers included relocation of mental health staff to non-mental health related COVID-19 tasks (structural); mental health service interruption (organizational); mental health staff on preventive/medical leave (provider); the pandemic’s effect on consultations (i.e., perceptions of increased demand) (patients); and challenges with the use of technological modalities (innovation). Facilitating factors included reinforcements to mental health care teams (structural); perceptions of reductions in wait times for mental health evaluations during the second wave due to diminished FP referrals in the first wave, as well as supports (i.e., management, private sector, mental health trained staff) for mental health service delivery (organizational); staff’s mental health consultation practices (provider); and advantages in increasing the use of technological modalities in practice (innovation). Conclusions To our knowledge, this is the first study to explore barriers and facilitating factors to mental health service delivery during the pandemic in Quebec, Canada. Some barriers identified were caused by the pandemic, such as the relocation of staff to non-mental health services and mental health service interruption. Offering services virtually seemed to facilitate mental health service delivery only for certain population groups. Recommendations related to building and strengthening human and technological capacity during the pandemic can inform mental health practices and policies to improve mental health service delivery in primary care settings and access to mental health services via access points.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".