A Qualitative Content Analysis of Sustainable Quality of Life Concept in Research Articles
Bibliographic record
Abstract
Sustainable Quality of Life is a new concept that emerged in socioeconomically wealthy countries with high importance given to the concepts of Quality of Life and Sustainability. It is evident that many macroeconomic aspects such as inflation, unemployment, economic growth, etc. impact the sustainability of quality of life. The aim of the study is to analyse the evolution of the concept of sustainable quality of life and to define the primary macroeconomic factors that affect the sustainable quality of life. The study was conducted as a qualitative content analysis of research articles, grounding the research questions of (a) what is sustainability, (b) what is quality of life, (c) what is the sustainable quality of life, and (d) how to achieve a sustainable quality of life. Categories were developed for each research question and the frequency of usage of the category was used to answer each question. It is observed that many types of research have been carried out to study sustainability, quality of life, sustainable quality of life, different indicators of sustainability, indicators of quality of life, and measurement of quality of life with different approaches. Innovation, research, creativity health, education and training, social relations, safety, environment, and quality of services contribute vastly to the achievement of sustainable quality of life. Further, it is observed that there are only a few research articles that have focused on how to achieve a sustainable quality of life and it is a broad concept that requires more attention and in-depth study.
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 | 0.038 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".