From personal crisis care to convenience shopping: an interpretive description of the experiences of people with mental illness and addictions in community pharmacies
Bibliographic record
Abstract
BACKGROUND: The role of community pharmacists is changing globally with pharmacists engaging in more clinically-oriented roles, including in mental health care. Pharmacists' interventions have been shown to improve mental health related outcomes but various barriers can limit pharmacists in their care of patients. We aimed to explore the experiences of people with lived experience of mental illness and addictions in community pharmacies to generate findings to inform practice improvements. METHODS: We used interpretive description methodology with analytic procedures of thematic analysis to explore the experiences of people with lived experience of mental illness and addictions with community pharmacy services. Participants were recruited through multiple mechanisms (e.g., paper and online advertisements), offered honorarium for their time, and given the option of a focus group or interview for participation in our study. Data were gathered during July to September of 2012. Interviews and focus groups were audio-recorded, transcribed verbatim, and analyzed by two researchers. RESULTS: We collected approximately nine hours of audio data from 18 individuals in two focus groups (n = 12) and six individual interviews. Fourteen participants were female and the average age was 41 years (range 24 to 57 years). Expectations, decision-making, and supports were identified as central themes underlying the community pharmacy experiences of people with lived experience of mental illness and addictions. Eight subthemes were identified including: relationships with pharmacy staff; patient's role in the pharmacist-patient relationship; crisis and triage; privacy and confidentiality; time; stigma and judgment; medication-related and other services; and transparency. CONCLUSIONS: People with lived experience of mental illness and addictions demonstrate a high regard and respect for pharmacist's knowledge and abilities but hold conservative expectations of pharmacy health services shaped by experience, observations, and assumptions. To some extent, expectation management occurs with the recognition of the demands on pharmacists and constraints inherent to community pharmacy practice. Relationships with pharmacy staff are critical to people with lived experience and influence their decision-making. Research in the area of pharmacists' roles in crises and triage, especially in the area of suicide assessment and mitigation, is needed urgently.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".