A structured needs assessment does not improve clinical outcomes for clients under the care programme approach
Bibliographic record
Abstract
Marshall M, Lockwood A, Green G, et al . Systematic assessments of need and care planning in severe mental illness: cluster randomised controlled trial. Br J Psychiatry 2004;185:163–8.[OpenUrl][1][Abstract/FREE Full Text][2] Q Do clients benefit if formulation of their care plan is based on a formal, standardised assessment of need? ### ![Graphic][3] Design: Randomised controlled trial. ### ![Graphic][4] Allocation: Not stated. ### ![Graphic][5] Blinding: Single blinded (assessor at 12 months was blind to group allocation). ### ![Graphic][6] Follow up period: 12 months. ### ![Graphic][7] Setting: National Health Service Trusts in urban areas of northwest England; October 1998 to October 2000. ### ![Graphic][8] Patients: 304 participants (in 72 clusters of up to six people under a given care coordinator); being cared for in the community under care programme approach (CPA); meeting Goldman’s criteria for severe mental disorder. ### ![Graphic][9] Intervention: The trial comprised two parallel experiments. In the first, clusters were randomised to structured assessment or control (experiment 1). In the second, individuals under a single care coordinator were randomised to structured assessment or control (experiment 2). Both groups … [1]: {openurl}?query=rft.jtitle%253DThe%2BBritish%2BJournal%2Bof%2BPsychiatry%26rft.stitle%253DBr.%2BJ.%2BPsychiatry%26rft.aulast%253DMARSHALL%26rft.auinit1%253DM.%26rft.volume%253D185%26rft.issue%253D2%26rft.spage%253D163%26rft.epage%253D168%26rft.atitle%253DSystematic%2Bassessments%2Bof%2Bneed%2Band%2Bcare%2Bplanning%2Bin%2Bsevere%2Bmental%2Billness%253A%2BCluster%2Brandomised%2Bcontrolled%2Btrial%26rft_id%253Dinfo%253Adoi%252F10.1192%252Fbjp.185.2.163%26rft_id%253Dinfo%253Apmid%252F15286069%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/ijlink?linkType=ABST&journalCode=bjprcpsych&resid=185/2/163&atom=%2Febmental%2F8%2F1%2F18.atom [3]: /embed/inline-graphic-1.gif [4]: /embed/inline-graphic-2.gif [5]: /embed/inline-graphic-3.gif [6]: /embed/inline-graphic-4.gif [7]: /embed/inline-graphic-5.gif [8]: /embed/inline-graphic-6.gif [9]: /embed/inline-graphic-7.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 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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".