Gillick competence model for Malaysian children / Abdullah Ali
Bibliographic record
Abstract
This research is classified as legal research since the research problem stems from lack of statutory rights for children aged between 14 and 18 years old to give or refuse to medical, dental and surgical treatments. The legal research is also fundamental in nature as it aims to develop a legal framework on the right of children in medical, surgical or dental treatments by introducing Gillick Competence model. Being a fundamental legal research, it adopts grounded theory approach which attempts to develop an understanding of the theories, laws and policies underpinning Gillick Competency, so as to enable Gillick Competence model for Malaysian children to be developed. To answer the three-tier research questions, this research employs qualitative research method for the purpose of data collection and data analysis. The research undertakes comparative analysis of the laws and policies of Scotland, Australia, Canada, and New Zealand that underpinned the right of the children to give consent or refuse the treatments. The proposed Gillick Competence model comprised of both substantive law and procedural law components. The proposed model covers both the right of the children to give consent and to refuse to consent, in three areas of healthcare practice i.e. medical, dental and surgical treatments. By developing such model, this research fills in the gaps of the existing law. This research also significantly contributes to the existing body of knowledge as it explores four major theories i.e. Will theory, Laissez-faire theory, Cognitive Development theory and Our Three Conditions theory of autonomy, underlying the right of the children aged between 14 to 18 years old in giving consent or refusing the treatments.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".