Traditional & narrative practices of treatment for depression and depressive symptoms in older adults
Bibliographic record
Abstract
An analysis and evaluation of the literature regarding traditional treatment methods for depression among older adults compared the effectiveness of the results to the benefits of a treatment plan that integrates the narrative practices of storytelling and reflexive writing. Priority was given to peer-reviewed journal articles from 2008 forward, though some earlier information was used for clarification and foundation building. The formation and implementation of individual patient treatment plans for depression and depressive symptoms are impacted by many variables such as: Confusion surrounding provider treatment guidelines, social organizational context, organizational climate and the differing definitions of depression that exist among providers and patients. Patients often struggle to self-identify or put words to depressive symptoms and the process of reflexive writing is transformative and increases narrative competency, which strengthens a patient’s ability to give an account of oneself, aiding in self-discovery and personal symptom awareness. An imbalance of power exists in the clinical encounter and the practices and principles of the discipline of Narrative Medicine can have a positive impact on strengthening the therapeutic alliance and treatment outcomes. Older adults with depression and depressive symptoms have a lower quality of life and often feel less productive in their communities. Traditional pharmacologically based depression treatment plans are one-dimensional and often fail to address personal patient context and preference. Older adults living with diagnosed depression and depressive symptoms can be better served with treatment plans that include narrative techniques that increase alliance, affiliation, self-awareness and self-discovery.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".