Research trends among new investigators at ISOQOL: a bibliometric analysis from 2019 to 2023
Bibliographic record
Abstract
BACKGROUND: New investigators (NI), encompassing graduate students, recent doctoral graduates, and early-career faculty, are instrumental in advancing quality of life (QoL) research through innovative methodologies and diverse perspectives. Within the International Society for Quality of Life Research (ISOQOL), the New Investigators Special Interest Group (NI-SIG) fosters collaboration and supports this community. This study utilizes bibliometric analysis to examine the contributions of NI-SIG members, focusing on publication trends, collaboration patterns, and thematic developments in QoL research. METHODOLOGY: Data on publications authored by 56 NI-SIG members between 2019 and 2023 were extracted from Web of Science and Scopus. A two-step screening process, guided by the Wilson and Cleary model of QoL, identified 561 unique documents for analysis. Descriptive metrics included publication trends, citations, journal impact factors, and geographic distribution, while network analysis explored co-authorship patterns. Thematic mapping was conducted using clustering algorithms to identify established and emerging research areas. RESULTS: Publication output rose steadily from 2019 to 2022, peaking at 163 publications before declining to 135 in 2023, accompanied by a reduction in average citations per document from 4.8 to 1.3. The majority of publications appeared in leading journals such as Quality of Life Research (n = 128), Journal of Patient-Reported Outcomes (n = 17), and BMJ Open (n = 15). Geographic analysis revealed that most contributors were from high-income countries, with the United States, Canada, and Australia accounting for over 50% of publications. Co-authorship network analysis highlighted a robust, interconnected cluster of authors, though opportunities remain to enhance global partnerships, particularly with low- and middle-income countries. Thematic analysis identified well-established areas, including psychometric validation and cancer, alongside emerging topics such as mixed methods in QoL research. CONCLUSION: This study highlights robust collaborations among NI-SIG members while identifying opportunities to enhance international collaboration and methodological innovation. Expanding partnerships with underrepresented regions and embracing advanced technologies such as natural language processing could foster inclusivity and drive transformative advancements in QoL measurement and application.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchBibliometrics Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.114 | 0.165 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".