Mental Health at Workplace: A Bibliometric Analysis of Literature from Canada
Bibliographic record
Abstract
The purpose of this paper is to identify and review the research themes in the area of mental health in workplace. I conduct a bibliometric analysis of 219 peer-reviewed articles specific to research conducted in Canada. The articles are extracted from EBSCO using the key words “mental health” and “workplace” and published between the years 2000 and 2020. A qualitative research technique – ‘co-occurrence of key words’ is used to identify the most relevant key words in the theoretical corpus of 219 articles. Most frequently occurring words are clustered together forming a research theme. Five research themes- healthcare management, organizational context and support, psychological issues, methodology & research design, and Participants are identified. This research makes a significant academic contribution in providing directions for future research on the topic of mental health in organizations. From the practitioner viewpoint, it draws the attention of healthcare professionals to some of the more recent practices in organizations that address the important issue of mental health.
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 | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.260 | 0.409 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".