“What else can we do?”—Provider perspectives on treatment‐resistant depression in late life
Bibliographic record
Abstract
BACKGROUND: Treatment-resistant depression in late-life (TRLLD) is common. Perspectives of primary care providers (PCPs) and psychiatrists treating TRLLD could give insights into the challenges and potential solutions for managing this condition. METHODS: To identify perspectives of providers who treat TRLLD, we conducted a qualitative descriptive study using semi-structured interviews with providers treating older adults with TRLLD in five locations across North America (i.e., Los Angeles, New York City, Pittsburgh, St. Louis, and Toronto). We conducted semi-structured interviews with 50 care providers (24 primary care providers [PCPs], 22 psychiatrists, and 4 depression care managers). Interviews elicited providers' perspectives on treatment options for TRLLD, including treatment within the primary care setting and referral to psychiatry, and sought suggestions for improvement. RESULTS: We identified four themes. (1) Treating TRLLD takes an emotional toll on providers; (2) existing psychiatric services are inadequate to meet the needs of patients with TRLLD, mainly because of lack of access; (3) PCPs often attempt to treat TRLLD, even when they are not comfortable doing so; and (4) to better meet the needs of patients with TRLLD, providers recommend integrated care models involving PCPs, psychiatrists, and psychotherapists, potentially made more feasible by the growth of telehealth. CONCLUSIONS: Findings from these qualitative interviews show the challenges in providing care for TRLLD. These findings can guide knowledge dissemination to psychiatrists, PCPs, policy-makers, and other stakeholders involved in the mental health system. They can also inform structural changes to clinical practice that may increase the implementation of the best treatment strategies across settings to improve long-term outcomes for TRLLD.
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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".