% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/orm_indicators.R
\name{orisma_indicators}
\alias{orisma_indicators}
\alias{WRDI}
\alias{RCS}
\alias{MGP}
\alias{ASS}
\alias{bridge_score}
\title{ORISMA bibliometric indicators}
\description{
ORISMA implements five original bibliometric indicators designed
specifically for occupational health and safety (OHS) evidence mapping.
Three are corpus-level indicators (WRDI, RCS, MGP) and two are
record-level indicators (ASS, Bridge Score).
\subsection{1. Worker-Risk Disconnection Index (WRDI)}{
\subsection{Definition}{

The WRDI measures the proportion of studies in a corpus that characterise
an occupational risk \strong{without} including direct worker exposure data.
A study is considered to have worker exposure data if its abstract
contains terms indicating real measurement of exposure in actual workers
under real working conditions (e.g. "worker exposure", "occupational
exposure", "breathing zone", "personal sampling", "field study",
"workplace measurement").
}

\subsection{Formula}{

For a given risk category \eqn{c}:
\deqn{WRDI_c = 1 - \frac{N_{workers,c}}{N_{total,c}}}
where \eqn{N_{workers,c}} is the number of studies in category \eqn{c}
that include worker exposure data, and \eqn{N_{total,c}} is the total
number of studies in that category.

The \strong{global WRDI} is computed across all records:
\deqn{WRDI_{global} = 1 - \frac{N_{workers}}{N_{total}}}
}

\subsection{Interpretation}{
\itemize{
\item \strong{WRDI = 0}: All studies include direct worker exposure data.
The body of evidence is fully connected to real workplace conditions.
\item \strong{WRDI = 1}: No study includes worker exposure data. The entire
literature characterises the risk technically (e.g. in simulated
environments, chambers, or in vitro) without measuring real exposure
in workers.
\item \strong{WRDI >= 0.8}: Critical alert. The evidence has very low direct
preventive transferability. On-site risk assessment is essential.
\item \strong{WRDI 0.5-0.8}: Attention required. More than half the evidence
lacks worker data.
\item \strong{WRDI < 0.3}: Reasonable coverage. Most studies include worker data.
}
}

\subsection{Important limitation}{

WRDI detection is based on abstract text, not full text. Studies that
measured worker exposure but did not mention it in the abstract may
be misclassified. Manual validation via \code{\link[=orm_validate]{orm_validate()}} is recommended.
}

}

\subsection{2. Risk Category Saturation Index (RCS)}{
\subsection{Definition}{

The RCS measures the \strong{relative dominance} of a risk category in the
corpus compared to a hypothetical uniform distribution across all
categories. It identifies which categories are over-represented
(saturated) and which are under-represented (gaps) in the literature.
}

\subsection{Formula}{

\deqn{RCS_c = \frac{pct_c}{pct_{uniform}}}
where \eqn{pct_c} is the percentage of records assigned to category
\eqn{c}, and \eqn{pct_{uniform} = 100 / K} is the percentage each
category would have under a uniform distribution across all \eqn{K}
categories.

Equivalently:
\deqn{RCS_c = \frac{N_c \cdot K}{N_{total}}}
where \eqn{N_c} is the number of records in category \eqn{c},
\eqn{K} is the total number of categories, and \eqn{N_{total}} is
the total number of records.
}

\subsection{Interpretation}{
\itemize{
\item \strong{RCS > 1}: The category is over-represented relative to a uniform
baseline. The literature has concentrated disproportionately on this
risk type.
\item \strong{RCS = 1}: The category has exactly the representation expected
under a uniform distribution.
\item \strong{RCS < 1}: The category is under-represented. This risk type has
received less attention than a balanced literature would suggest.
\item \strong{RCS = 0}: No studies address this category. Complete evidence gap.
}
}

\subsection{Note}{

RCS is a relative measure. A category can have RCS > 1 with very few
absolute studies if the corpus is small or highly specialised. Always
interpret RCS together with the absolute number of records (N).
}

}

\subsection{3. Material-Gap Profile (MGP)}{
\subsection{Definition}{

The MGP is a domain-specific indicator designed for corpora where the
corpus can be stratified by material, substance, or agent. It measures
the ratio between a material's known hazard potential and its coverage
in the occupational health literature, identifying materials that are
dangerous but understudied.
}

\subsection{Formula}{

\deqn{MGP_m = \frac{hazard\_proxy_m}{coverage_m}}
where \eqn{hazard\_proxy_m} is an estimate of the material's hazard
potential (based on the number of distinct risk categories detected in
studies involving that material), and \eqn{coverage_m} is the proportion
of corpus records that address that material.
}

\subsection{Interpretation}{
\itemize{
\item \strong{High MGP}: The material is associated with multiple risk categories
but appears in few studies. Priority material for future research and
on-site risk assessment.
\item \strong{Low MGP}: The material is well-covered in the literature relative
to its known hazard profile.
\item \strong{MGP requires a material column}: The \code{material_col} parameter in
\code{\link[=orm_analyse]{orm_analyse()}} must point to a column classifying each record by
material or agent. If not available, MGP is not computed.
}
}

}

\subsection{4. Abstract Sufficiency Score (ASS)}{
\subsection{Definition}{

The ASS is a \strong{cumulative hierarchical index} (0-5) measuring how much
preventively useful information an abstract contains for an occupational
health practitioner. It is not a measure of study quality, but of
abstract informativeness for preventive purposes.

The score is strictly cumulative: a record cannot reach level N without
satisfying all previous levels.
}

\subsection{Levels}{

\describe{
\item{0 - Non-informative}{The abstract contains no hazard or risk
terms relevant to OHS. No useful preventive information.}
\item{1 - Hazard without context}{The abstract mentions a hazard or
risk agent (e.g. nanoparticles, noise, vibration) but provides no
occupational or workplace context. Could be an environmental or
laboratory study.}
\item{2 - Occupational context}{The abstract mentions workers,
employees, operators, or workplace/occupational setting. The study
is clearly situated in a work context.}
\item{3 - Exposure measurement}{The abstract reports quantitative
exposure data: concentrations, levels, measurements, or monitoring
results. Implies some form of exposure quantification.}
\item{4 - Worker exposure with result}{The abstract explicitly
reports exposure in workers (not just in the environment) with a
result (e.g. exceeded a limit, found significant association,
detected at breathing zone).}
\item{5 - Complete preventive abstract}{The abstract addresses all
four dimensions: worker population + exposure measurement + study
method/design + preventive recommendation or control measure. This
is the highest OHS informative level.}
}
}

\subsection{Computation}{

Each level is detected via regular expression patterns applied to the
abstract text. Detection is strictly cumulative: the algorithm tests
each level in sequence and stops at the first level not satisfied.
}

\subsection{Interpretation}{
\itemize{
\item \strong{Mean ASS < 2}: The corpus is predominantly technical with very
little preventive context. High priority for on-site investigation.
\item \strong{Mean ASS 2-3}: Mixed corpus. Some workplace context but limited
quantitative exposure data.
\item \strong{Mean ASS > 3}: Good preventive evidence base. Substantial
proportion of studies report actual worker exposure data.
\item \strong{ASS = 5 articles}: These are the most valuable abstracts for
practitioners and should be read in full first.
}
}

}

\subsection{5. Bridge Article Score}{
\subsection{Definition}{

A bridge article is a study that \strong{connects technical science with
applied OHS prevention}. It simultaneously addresses five dimensions
that are rarely all present in a single study:

\describe{
\item{Criterion 1 - Technology/process}{The study involves a specific
technology, industrial process, or work task (e.g. additive
manufacturing, welding, construction, healthcare).}
\item{Criterion 2 - Hazardous agent}{The study characterises a
specific hazardous agent (chemical, physical, biological, or
psychosocial).}
\item{Criterion 3 - Workers (MANDATORY)}{The study involves a real
worker population in a real workplace setting. This criterion is
mandatory for bridge classification.}
\item{Criterion 4 - Exposure measurement (MANDATORY)}{The study
quantitatively measures exposure (air sampling, biological
monitoring, dosimetry, etc.). This criterion is mandatory for
bridge classification.}
\item{Criterion 5 - Prevention/recommendation}{The study includes
preventive recommendations, control measures, or intervention
results.}
}
}

\subsection{Classification}{

\describe{
\item{Strong bridge (score 4-5)}{Meets criteria 3+4 (mandatory) plus
2 or 3 additional criteria. Highest priority for full-text reading.
These articles have already done the translation from laboratory
science to workplace prevention.}
\item{Partial bridge (score 3)}{Meets criteria 3+4 (mandatory) plus
1 additional criterion. Valuable but incomplete bridge.}
\item{Technical study (score 0-2, or missing C3/C4)}{Does not meet
the mandatory criteria. Contributes technical knowledge but lacks
direct preventive applicability.}
}
}

\subsection{Priority score}{

The overall \strong{priority reading score} used in \code{\link[=orm_ranking]{orm_ranking()}} combines
all record-level indicators:
\deqn{Priority = (Bridge \times 2) + (ASS \times 1.5) + (N_{cats} \times 0.5)}
where \eqn{N_{cats}} is the number of risk categories detected in the
record. Bridge score is weighted highest because it reflects the most
direct preventive relevance.
}

}

\subsection{References}{

The WRDI, RCS, and MGP indicators were first described in:

Aguilar-Elena, R. & Delgado-Garcia, A. (2025). \emph{Mapping the Safety
Landscape of Emerging Technologies: A Bibliometric Analysis of
Occupational Risks in Metal Additive Manufacturing}. (Under review)

The ORISMA methodological framework is described in:

Aguilar-Elena, R. & Delgado-Garcia, A. (2025). \emph{orisma: A Framework
for Occupational Risk Integrated Systematic Mapping and Analysis}.
R package version 0.1.0. Universidad Internacional de Valencia (VIU) &
Universidad de Salamanca (USAL).
}
}
\seealso{
\code{\link[=orm_analyse]{orm_analyse()}} to compute WRDI, RCS, and MGP.
\code{\link[=orm_ass]{orm_ass()}} to compute the Abstract Sufficiency Score.
\code{\link[=orm_bridge]{orm_bridge()}} to detect bridge articles.
\code{\link[=orm_ranking]{orm_ranking()}} to generate a priority reading list.
\code{\link[=orm_validate]{orm_validate()}} to validate automatic classification with Cohen's Kappa.
}
