Factors That Impact Treatment Decisions
Bibliographic record
Abstract
OBJECTIVE: To identify patient-reported factors that influence medication treatment decisions among individuals with bipolar and unipolar depression. METHODS: The Depression and Bipolar Support Alliance (DBSA) conducted an online survey February 2016 to April 2016 asking participants about factors that influence treatment decisions (eg, starting and stopping specific medications). RESULTS: In total, 896 participants completed the survey (49.9% unipolar depression [n = 447] and 50.1% bipolar depression [n = 449]). The majority of respondents reported several previous medication trials. The most frequently reported factors impacting treatment decisions were side effects, doctor recommendations, cost, and how quickly the treatment will begin to work. The most common reason for changing treatments was ineffectiveness in the unipolar depression group and side effects in the bipolar depression group. Weight gain was the side effect that most commonly led respondents to discontinue a medication. When respondents currently using medications versus respondents not using medications were compared, doctor recommendations were more likely to be influential for those taking medications (P < .0001). Conversely, cost (P = .008) and impact on pregnancy/lactation (P = .045) were more likely to impact treatment decisions in participants not currently taking medications. Current medication use was associated with increased rates of perceived treatment effectiveness (P < .0001). CONCLUSIONS: Side effects, doctor recommendations, cost, and rapidity of antidepressant effects were determined to be particularly important factors in making treatment decisions, with doctor recommendations being more influential for medication users and cost being more influential for participants not using medications. These findings highlight the importance of patient-centered factors in adjudicating treatment decisions.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".