COVID-19 pandemic and access to mental healthcare: A qualitative study of the experiences of mental healthcare providers and caregivers in Ghana
Bibliographic record
Abstract
The COVID-19 pandemic created enormous additional demand for already weak mental health systems in many countries that were severely impacted, as evidence showed that new mental health disorders were triggered while those with pre-existing mental health conditions worsened. Yet, we know little about the extent to which the pandemic impacted the provision of care by frontline mental health care providers and access to mental health care among caregivers. This study describes the experiences of frontline mental health care providers and caregivers during the COVID-19 pandemic in Ghana. A qualitative study employing a phenomenological approach and semi-structured interview methods was undertaken among caregivers of people living with any kind of mental illness and mental health care providers. We used a purposive sampling technique to select study participants who were available and consented to share their experiences. An interview guide facilitated the data collection process. We used an inductive approach through open coding to generate codes. We then grouped codes into emergent categories and further into themes of experiences of accessing mental health services by users and provision of mental health services by providers. Our analysis identified six themes regarding frontline mental health care providers' experiences at the primary health care level during the COVID-19 pandemic: a) interrupted home visits b) lack of government support c) lack of medication d) low clinic attendance e) disruptions in service delivery f) extended medication supply and g) patient aggression and attacks. Four themes emerged that characterize the experiences of caregivers of people living with mental health conditions, namely a) denial and withdrawal of facility-based care b) medication refill challenges c) disruption in outreach and follow-up calls and d) isolation and limited community support. The study highlights the challenges experienced by frontline mental health care providers and caregivers of people living with mental health conditions. It brought to light specific gaps that require urgent attention for people living with mental health conditions, their caregivers, and frontline workers. Institutional additional support systems to ensure fewer disruptions and address access-based gaps through outreach and community support would help improve their experiences in future pandemics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".