Creating a Clinical Care Pathway for Depressive Symptoms and Disorders in Long-Term Care: A Modified Delphi Process
Bibliographic record
Abstract
OBJECTIVES: To create an evidence- and expert-informed clinical care pathway focused on identifying and treating depressive symptoms and disorders in long-term care (LTC) residents. DESIGN: Modified Delphi survey. SETTING AND PARTICIPANTS: Delphi participants were LTC health care providers, LTC administrators, friend/family caregivers of residents living in LTC, and residents of LTC. METHODS: Initial survey statements were developed based on evidence and expert opinion. The survey was distributed in as many rounds as required to reach agreement among participants. Survey participants used Likert scale responses to rate their agreement with each statement describing a step of the clinical care pathway. Statements were revised between rounds based on participant feedback and median and interquartile range values. RESULTS: To reach a consensus among participants on statements, 2 rounds of survey distribution were required. Twenty-six participants completed both rounds of the survey. Statements were organized into 4 categories: depression detection, identifying contributors to depression, symptom management, and coordination of care. Clinicians identified critical statements that were considered foundational to pathway function. CONCLUSIONS AND IMPLICATIONS: The generated statements provide steps for identifying and managing depression among residents of LTC. These steps can be further tested in practice Canada-wide to improve care for residents. Steps that detail care outside current practice, such as staff education on depression detection and access to nonpharmacologic treatments, may require additional resources. During statement revisions, participants disagreed on when depression should be assessed, how validated depression assessment tools should be used, and how to account for depressive history and comorbidities as part of management. Further research is required to understand the barriers to providing care for depression before pathway implementation.
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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 | 0.186 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".