Nurse telehealth care reduced depressive symptoms and improved functioning
Bibliographic record
Abstract
Hunkeler EM, Meresman JF, Hargreaves WA, et al. Efficacy of nurse telehealth care and peer support in augmenting treatment of depression in primary care. Arch Fam Med2000 Aug; 9 : 700 –8 [OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] QUESTIONS: In depressed patients, is adjunctive nurse telehealth care more effective in reducing depressive symptoms than usual physician care alone? Does the addition of peer support to nurse telehealth care further improve outcomes? Randomised (unclear allocation concealment*), unblinded,* controlled trial with 6 months of follow up. Primary care clinics in northern California, USA. 302 patients (mean age 55 y, 69% women) who had major depressive disorder or dysthymia and had a prescription for a selective serotonin reuptake inhibitor (SSRI). Exclusion criteria were antidepressant drug prescription in the previous 6 months, inadequate command of English, substance abuse, current suicide risk, or reported thoughts of violence. Follow up was 90% at 6 weeks and 85% at 6 months. Patients were allocated to usual physician … [1]: {openurl}?query=rft.jtitle%253DArchives%2Bof%2BFamily%2BMedicine%26rft.stitle%253DArch%2BFam%2BMed%26rft.aulast%253DHunkeler%26rft.auinit1%253DE.%2BM.%26rft.volume%253D9%26rft.issue%253D8%26rft.spage%253D700%26rft.epage%253D708%26rft.atitle%253DEfficacy%2Bof%2BNurse%2BTelehealth%2BCare%2Band%2BPeer%2BSupport%2Bin%2BAugmenting%2BTreatment%2Bof%2BDepression%2Bin%2BPrimary%2BCare%26rft_id%253Dinfo%253Adoi%252F10.1001%252Farchfami.9.8.700%26rft_id%253Dinfo%253Apmid%252F10927707%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1001/archfami.9.8.700&link_type=DOI [3]: /lookup/external-ref?access_num=10927707&link_type=MED&atom=%2Febmental%2F4%2F2%2F49.atom [4]: /lookup/external-ref?access_num=000088702500008&link_type=ISI
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 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 | 0.000 | 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 | 0.001 | 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) | 0.001 | 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 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".