Bibliographic record
Abstract
Abstract Quality of life (QOL) has been developing in the field of IDD since the early 1980s, and ever since there have been research, models, and theoretical constructs along with many recommendations. Ignored in its early development, QOL is now seen as important for support and intervention. The research has resulted in new insights yet there remain many challenges, three of which are discussed in this article. (1) Much QOL research requires the acceptance of parent and allied commentary that is regarded as subjective and frequently carries less weight than objective evidence. This can raise questions across disciplines regarding the validity and therefore the value of QOL in the field of research, practice, and policy. (2) Family quality of life (FQOL) research, which is an outgrowth of QOL in IDD, has resulted in a number of questions concerning our perception and management of family challenges. One is our understanding or lack of understanding of the process of inclusion, which is discussed suggesting the need for a much more clear articulation of exclusion and inclusion and its relevance to research and application within a QOL context. (3) QOL involves an holistic approach and much of this approach has been researched and applied in the field of IDD. It is posited in this article that the QOL approach should now be seen as a paradigm for research, policy, and intervention in which other procedures can be explored and addressed. To do so the paradigm requires further development and integration and an understanding of its specificity and breadth of potential application. Each of these issues is discussed and recommendations are put forward for action under the headings of Perceptual and Objective Data, Education of Personnel, Further Research and Application, and Policy Integration.
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 | 0.113 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.009 | 0.038 |
| Scholarly communication | 0.036 | 0.028 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.021 | 0.030 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".