Correction: An Environmental Scan of Sex and Gender in Electronic Health Records: Analysis of Public Information Sources (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED Historically, the terms sex and gender have been used interchangeably as a binary attribute to describe a person as male or female, even though there is growing recognition that sex and gender are distinct concepts. The lack of sex and gender delineation in electronic health records (EHRs) may be perpetuating the inequities experienced by the transgender and gender nonbinary (TGNB) populations. This study aims to conduct an environmental scan to understand how sex and gender are defined and implemented in existing Canadian EHRs and current international health information standards. We examined public information sources on sex and gender definitions in existing Canadian EHRs and international standards communities. Definitions refer to data element names, code systems, and value sets in the descriptions of EHRs and standards. The study was built on an earlier environment scan by Canada Health Infoway, supplemented with sex and gender definitions from international standards communities. For the analysis, we examined the definitions for clarity, consistency, and accuracy. We also received feedback from a virtual community interested in sex-gender EHR issues. The information sources consisted of public website descriptions of 52 databases and 55 data standards from 12 Canadian entities and 10 standards communities. There are variations in the definition and implementation of sex and gender in Canadian EHRs and international health information standards. There is a lack of clarity in some sex and gender concepts. There is inconsistency in the data element names, code systems, and value sets used to represent sex and gender concepts across EHRs. The appropriateness and adequacy of some value options are questioned as our societal understanding of sexual health evolves. Outdated value options raise concerns about current EHRs supporting the provision of culturally competent, safe, and affirmative health care. The limited options also perpetuate the inequities faced by the TGNB populations. The expanded sex and gender definitions from leading Canadian organizations and international standards communities have brought challenges in how to migrate these definitions into existing EHRs. We proposed 6 high-level actions, which are to articulate the need for this work, reach consensus on sex and gender concepts, reach consensus on expanded sex and gender definitions in EHRs, develop a coordinated action plan, embrace EHR change from socio-organizational and technical aspects to ensure success, and demonstrate the benefits in tangible terms. There are variations in sex and gender concepts across Canadian EHRs and the health information standards that support them. Although there are efforts to modernize sex and gender concept definitions, we need decisive and coordinated actions to ensure clarity, consistency, and competency in the definition and implementation of sex and gender concepts in EHRs. This work has implications for addressing the inequities of TGNB populations in Canada.
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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 | 0.013 | 0.308 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.069 | 0.033 |
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".