Characteristics of in-patient versus out-patient drop outs in addiction treatment / Michael Bryson. --
Bibliographic record
Abstract
The relative merits of in-patient and out-patient treatment of \nsubstance abuse have been widely debated. For severe, chronic \nclients, the best form of treatment may be intensive in-patient \ncare. Less severe clients may fare better with out-patient \ntreatment. \nRegardless of the type of treatment, clients' drop-out rates \nare high. Since the client may be three times as likely to be free \nfrom drugs one year later if they complete treatment, serious \nattempts need to made to determine the factors affecting client \ndrop-out. \nThe research examined this issue by means of an archival \nsearch of client records from the Lakehead Addiction Centre \ntreatment program at the Lakehead Psychiatric Hospital (LPH) in \nThunder Bay, Ontario. The demographic, personality, and social \nstability characteristics related to drop-out of clients who had \nattended either the in-patient or out-patient program were \nexamined. Treatment drop-outs were studied for 98 \nout-patients and 406 in-patients. \nThis study confirms research which found a high rate of early \nattrition from treatment for substance-abusing clients. The \nresults indicate that treatment completers in either program \ndiffered significantly from non-completers by: patient type \n(P<0.05), use of LSD (P<0.01), and treatment mandated (P<0.05). \nOut-patients had significantly more completers. This may be due to \nthe significant differences between in-patient and out-patient attenders. These differences included: social support (P<0.01), \nattendance at AA/NA (P<0.01), and maximum drug intake per day or \nbinge (P<0.05). Natives were found to be significantly more likely \nto drop-out of either treatment (P<0.01).
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".