Bibliographic record
Abstract
den Boer PCAM, Wiersnia D, van den Bosch RJ. Why is self-help neglected in the treatment of emotional disorders? A meta-analysis. Psychol Med 2004;34:959–71.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q How effective are self-help interventions for people with clinically significant emotional disorders? ### ![Graphic][5]</img>Design: Systematic review. ### ![Graphic][6]</img>Data sources: MEDLINE, PsychINFO, and the Cochrane Library searched (1990–2000). Earlier studies (1970s to 1990) identified using previously published meta-analyses of self-help strategies. ### ![Graphic][7]</img>Study selection and analysis: Randomised controlled trials (RCTs) comparing self-help (bibliotherapy or self-help group) with placebo, waiting list, or treatment as usual in people with clinically significant emotional disorders were eligible for inclusion. Only studies using symptom measures or structured clinical interviews (DSM or ICD criteria) to identify participants were included. The Delphi criteria list was used to assess study quality. Excluded were: trials in people with mild emotional disorders not affecting wide areas of social functioning, trials in children or adolescents only. Meta-analysis was conducted using META version 5.3. A mean effect size was calculated for studies assessing multiple outcomes. Tests for heterogeneity and sensitivity analyses were carried out. ### ![Graphic][8]</img>Outcomes: Effect size (Cohen’s d ) difference … [1]: {openurl}?query=rft.jtitle%253DPsychological%2Bmedicine%26rft.stitle%253DPsychol%2BMed%26rft.aulast%253Dden%2BBoer%26rft.auinit1%253DP.%2BC.%26rft.volume%253D34%26rft.issue%253D6%26rft.spage%253D959%26rft.epage%253D971%26rft.atitle%253DWhy%2Bis%2Bself-help%2Bneglected%2Bin%2Bthe%2Btreatment%2Bof%2Bemotional%2Bdisorders%253F%2BA%2Bmeta-analysis.%26rft_id%253Dinfo%253Adoi%252F10.1017%252FS003329170300179X%26rft_id%253Dinfo%253Apmid%252F15554567%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=10.1017/S003329170300179X&link_type=DOI [3]: /lookup/external-ref?access_num=15554567&link_type=MED&atom=%2Febmental%2F8%2F2%2F44.atom [4]: /lookup/external-ref?access_num=000224104300001&link_type=ISI [5]: /embed/inline-graphic-1.gif [6]: /embed/inline-graphic-2.gif [7]: /embed/inline-graphic-3.gif [8]: /embed/inline-graphic-4.gif
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".