PREVALENCE, ROLE AND FUNCTIONS OF SELF-HELP GROUPS IN HONG KONG
Bibliographic record
Abstract
Self-help is a respectable Chinese value. The rapid growth of self-help groups in the field of social welfare in Hong Kong however, is only a recent phenomenon (Chau, 1996; Chow, 1996). Self-help groups for vulnerable populations such as patients with chronic illnesses, the physically disabled, the mentally disabled, single parents, drug abusers, and other client groups mushroomed in the late 1980s. The recognition and support given to self-help groups in the 1990 Social Welfare White Paper and the 1992 Green Paper on Rehabilitation Policies and Services have in part accelerated the development of self-help groups in the last few years. In spite of the rapid growth of self-help groups in Hong Kong, the full potential of utilising self-help groups remains untapped. One of the reasons may be attributed to the lack of systematic research and solid methodological foundations. Little was known about how helpful self-help groups are to members, and what kind of social impact they have brought about. The research component of the selfhelp phenomenon has clearly been left behind by the self-help movement itself. On the other hand, research on self-help groups has been flourishing in Western countries, particularly the United States and Canada (Borkman, 1976; Gartner & Riessman, 1977; Gottlieb, 1982; Kurtz, 1988; Humphreys, 1997; Roberts et al., 1999). There is definitely a need to systematically study the self-help phenomenon in the local context. Against this background and supported by a grant from RGC, this study was carried out from 1 October 1998 to 31 August 2001 with the following objectives.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".