Issues and Challenges in Performing Family Impact Analysis – Implications for <scp>H</scp>ong <scp>K</scp>ong
Bibliographic record
Abstract
Family impact analysis (FIA) is informed by a growing vision of incorporating a family perspective into policy making. It is a skillful and technical exercise in examining past, present, or probable future consequences – both intended and unintended – of a policy on family well‐being. Originating in the USA in the 1970s, it has become state/provincial or federal policy in the USA, Canada (Alberta), Australia, and Hong Kong. The policy has had different degrees of success in these countries. This article reviewed the critical issues and challenges for implementing the policy and the countries’ different responses to the challenges. It also discussed the implications of these international experiences on Hong Kong's policy of FIA as the newest member of the ranks. Attention to the diversity of families in defining family, adequate training and support to policy staff, building of quality control mechanisms, and the development of grassroots and political support of FIA were recommended. The review also found that there was a dearth of evaluation research on the policy. Further studies are necessary to examine whether FIA is an effective means of affecting policy‐planning decisions. It is a shared obligation of all countries that voted for a policy on FIA.
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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 | 0.245 | 0.299 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".