orisma

Occupational Risk Integrated Systematic Mapping and Analysis

orisma is an R package for systematic bibliometric mapping of occupational risk evidence.

It is designed for researchers, occupational safety and health professionals, industrial hygienists, ergonomists, psychosocial risk specialists and prevention practitioners who need to understand whether the scientific literature on a given topic is actually connected to workers, workplaces, exposure conditions and preventive decision-making.

Unlike general bibliometric tools, orisma focuses on the preventive usefulness of scientific evidence. It does not only count publications or keywords. It helps identify whether a research field is technically abundant but weakly connected to real occupational exposure, workplace tasks or preventive action.

Why ORISMA?

Emerging technologies, new work processes and complex occupational hazards often generate a large scientific literature before their real workplace risks are fully understood.

This creates a practical and methodological problem:

A topic may appear well studied, but the available evidence may still lack data on workers, real exposure conditions, tasks, sectors, controls or preventive recommendations.

orisma was created to detect this gap.

It helps answer questions such as:

• Is the literature technically abundant but weakly connected to real workers?
• Which occupational risk categories are over-represented or under-represented?
• Which articles are most useful for occupational risk assessment?
• Which studies connect technical science with applied prevention?
• Which risks require on-site assessment because the literature lacks worker-level evidence?
• Which records are probably off-topic, biomedical, clinical or weakly occupational and should be reviewed manually?

What does ORISMA do?

Starting from reference files exported from major bibliographic databases such as Web of Science, Scopus, PubMed, Dimensions, EBSCO and others, orisma runs a complete workflow.

Processing time depends on corpus size, file format, deduplication complexity and the number of risk categories analysed.

1. Ingestion — reads RIS, BibTeX and CSV files from multiple databases.
2. Deduplication — applies a three-step pipeline: exact DOI, normalised title and fuzzy matching.
3. Relevance guard — flags or excludes records with weak topic or occupational relevance.
4. Risk extraction — scans titles, abstracts and keywords against a 58-category occupational risk dictionary.
5. Bibliometric analysis — generates matrices, temporal trends, co-occurrence structures and risk distributions.
6. Preventive indicators — computes WRDI, RCS, MGP, ASS and Bridge Article Score.
7. Priority ranking — identifies articles with higher preventive usefulness.
8. Reports — generates bilingual HTML reports, practitioner risk sheets, extraction matrices and validation samples.

Main preventive bibliometric indicators

Worker-Risk Disconnection Index (WRDI)

The Worker-Risk Disconnection Index measures the proportion of studies that characterise a risk without reporting direct data on workers or workplace exposure.

A high WRDI suggests that the literature is technically developed but weakly connected to real working conditions.

WRDI is not a substitute for expert judgement. It is a signal that helps prioritise deeper review.

Risk Category Saturation Index (RCS)

The Risk Category Saturation Index measures whether a risk category is over-represented or under-represented compared with a uniform distribution across the dictionary.

It helps identify:

• dominant risk categories in the corpus;
• under-studied risk categories;
• risk areas where literature volume may not match preventive relevance;
• potential evidence gaps.

Material-Gap Profile (MGP)

The Material-Gap Profile is designed for corpora where records can be stratified by material, substance or agent.

It helps identify materials or agents that appear hazardous but remain poorly covered in the occupational health literature.

This is especially useful for topics such as:

• metal additive manufacturing;
• nanomaterials;
• battery manufacturing;
• advanced materials;
• chemical agents;
• biological agents;
• emerging technologies.

Abstract Sufficiency Score (ASS)

The ASS is a cumulative 0-5 score measuring how much preventively useful information an abstract contains.

The ASS is not a measure of study quality. It is a measure of how informative the abstract is for occupational prevention.

Bridge articles

A bridge article connects technical science with applied occupational prevention.

It usually combines:

1. A technology, process or work task.
2. A hazardous agent or risk factor.
3. A real worker population or workplace setting.
4. Exposure measurement or workplace assessment.
5. Preventive recommendations or control measures.

Bridge articles are useful because they help practitioners move from general scientific evidence to concrete preventive action.

Installation

# From CRAN (once published)
install.packages("orisma")

# Development version from GitHub
# install.packages("remotes")
remotes::install_github("Aguilar-Elena/orisma")

Minimal usage — 3 lines

library(orisma)

refs   <- orm_load("my_references/")   # load RIS/BibTeX/CSV files
result <- orm_run(refs)                 # full pipeline (2-3 sec)
orm_report(result, lang = "en")         # generate all outputs

For Spanish output:

options(orisma.lang = "es")
refs   <- orm_load("mis_referencias/")
result <- orm_run(refs)
orm_report(result, lang = "es", out_dir = "resultados/")

Complete function reference

Outputs generated automatically

After running orm_report() and orm_risk_sheet():

For researchers

For OHS practitioners

Risk dictionary

The built-in dictionary covers 58 occupational risk categories in 6 blocks.

The dictionary can be extended for any domain:

dict <- orm_dict()

# Add terms to an existing category
dict <- orm_dict_add_terms(dict, "nanomaterials", c("nano-aerosol", "NOAA"))

# Add a completely new category
dict <- orm_dict_add_category(dict,
  key      = "exoskeleton_risk",
  label_en = "Exoskeleton-related musculoskeletal risk",
  label_es = "Riesgo musculoesqueletico por exoesqueleto",
  terms    = c("exoskeleton", "powered exosuit", "wearable robot")
)

Supported databases

Export all databases in RIS format, place files in a folder, and run orm_load("folder/"). ORISMA detects the source database automatically from the filename.

Abstract Sufficiency Score (ASS)

The ASS is a cumulative 0-5 index measuring how much preventively useful information an abstract contains:

Bridge articles

A bridge article connects technical science with applied OHS prevention. It simultaneously addresses:

1. Technology or process
2. Hazardous agent
3. Workers (real workplace population)
4. Exposure measurement
5. Preventive recommendation

Articles meeting 4-5 criteria = Strong bridge (highest priority for reading). Articles meeting 3 criteria (must include workers + measurement) = Partial bridge.

Methodological note

ORISMA uses dictionary-based automatic classification. This may produce false positives. Manual validation of a representative sample is recommended using orm_validate(), which computes Cohen’s Kappa between automatic and manual classification. A Kappa >= 0.7 is acceptable for high-impact journal publication.

ORISMA does not include country-specific regulations or limit values, as these vary by jurisdiction. The practitioner applies the relevant national/regional regulation based on the risk categories identified.

Limitations

ORISMA relies primarily on bibliographic metadata, titles, abstracts and keywords. It may miss information that appears only in the full text.

Automatic classification may produce false positives or false negatives, especially when terms are used differently across disciplines. This is why ORISMA includes a relevance guard and a validation workflow.

WRDI, ASS and Bridge Score should be interpreted as prioritisation and mapping indicators, not as definitive quality assessment tools.

Country-specific legal requirements, occupational exposure limits and regulatory thresholds are not embedded in ORISMA because they vary by jurisdiction. Practitioners should apply the relevant national or regional legislation after identifying the risk categories.

Citation

If you use orisma in your research, please cite:

Aguilar-Elena, R. & Delgado-Garcia, A. (2025). orisma: Occupational Risk
Integrated Systematic Mapping and Analysis. R package version 0.1.0.
Universidad Internacional de Valencia (VIU) & Universidad de Salamanca (USAL).
https://github.com/Aguilar-Elena/orisma

Authors

PhD. Raul Aguilar-Elena · raguilar@universidadviu.com
Occupational Risk Prevention and Occupational Health Research Group (GPRL)
Universidad Internacional de Valencia (VIU), Valencia, Spain

Ana Delgado-Garcia · a.delgado@usal.es
Universidad de Salamanca (USAL), Salamanca, Spain

License

MIT © 2025 Raul Aguilar-Elena & Ana Delgado-Garcia