Everyday life information seeking: A systematic review with bibliometric analysis
Bibliographic record
Abstract
This study presents the first comprehensive systematic review and bibliometric analysis of the Everyday Life Information Seeking (ELIS) research domain, spanning nearly three decades of academic contributions. Initiated by Savolainen’s groundbreaking study in 1995, ELIS research has significantly progressed; however, to date, no researchers have undertaken a thorough overview of this field. Employing a mixed-methods approach, this study combines systematic review techniques with bibliometric analysis, aiming to bridge this gap. The methodology includes rigorous data filtration from the Web of Science and Scopus databases, amassing a total of 345 articles published between 1995 and 2023. Findings indicate a marked increase in ELIS publications and citations, particularly after 2008, underscoring a growing interest in everyday life information seeking. Analysis identified significant contributions from the United States, Canada, the United Kingdom, Finland, and Australia. Thematic analysis unveiled three main research directions: the influence of digital technology on ELIS, information seeking in health scenarios, and diverse information needs and sources among specific user groups. Furthermore this study also proposes an integrative framework, emphasizing the importance of understanding information-seeking behaviors within the complex and diverse context of everyday life. It recognizes the necessity of incorporating various sociocultural perspectives and adapting to the continuously evolving digital environment. This framework serves as a valuable resource for scholars and practitioners dedicated to improving information services and interventions in daily life. The limitations of this study include language and database restrictions, as well as the exclusion of gray literature. Additionally, given the significant potential of integrating information seeking with technology to improve daily life, future research should focus on the foundational theoretical issue of how artificial intelligence technology is reshaping users’ ELIS behaviors.
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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 | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | 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 | 0.029 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.133 | 0.105 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".