This disclosure asks an organisation to report what share of its employees had a regular review of their performance and career development during the reporting period. In practice, it is about showing how widely these reviews are used across the workforce, rather than simply saying that a review process exists.
The main focus is coverage: whether the organisation applies these reviews across its operations and employee groups, or only in certain parts of the business such as head office or flagship sites. A useful report will make clear the basis of the percentage, so readers can understand who was included and how complete the process is.
This LRA educational guidance supports disclosure preparation. For the exact requirements, always refer to the official GRI source.
A quick mental checklist before you prepare this disclosure — tick each as you settle it.
Key datapoints to prepare
How to prepare it
Request the review coverage data from HR / People Analytics
Translate the disclosure into an internal business question — then adapt it to your organisation's own language.
Use your organisation’s own labels first, then map them to the reporting categories. For example, if you talk about appraisal, check-in, talent review, development conversation or one-to-one review internally, use those terms in the request and only translate them afterwards for reporting.
Can you send the GRI 404-3 data showing the the evidence needed for GRI 404:GRI 404-3, split by gender and employee category?
Why it fails: It uses reporting-framework language first, which can force the owner to translate before they can recognise their own data. It also leaves out the population, source, counting basis, and the internal name of the review process, so the result may be hard to verify or reconcile.
Can you send the review coverage extract for [reporting period] from [system/file]? Please show, for each internal employee group and gender, how many people were in scope, how many had a routine performance/development conversation during the period, and the resulting percentage. Include the population rules, exclusions, counting method, and extract date. I’ll map your terms to the reporting categories after I receive it.
Notes that turn data into a disclosure
LRA training templates — adapt them to your organisation, and check the official source before sign-off.
State how you defined the employee groups and the review measure, and note whether the percentage is based on headcount or another agreed workforce basis for the reporting period.
Explain that the figures show how widely formal development conversations were used across the workforce, and that differences between groups can point to uneven access or inconsistent practice.
If the figures moved materially from the prior period, link the change to practical drivers such as changes in workforce mix, coverage of the review process, or timing of the assessment cycle.
Preparation tools & forms
Professional preparation tools for GRI 404-3 — free with an LRA Community membership. Register once (it's free) and every download unlocks, together with the Disclosure Library, templates and the LRA AI-assistant.
For each claim, check the evidence
Evidence pack to prepare
Common reporting gaps
Mistakes to avoid when collecting the data
Where judgement is often needed
Illustrative examples
Synthetic, written by LRA — not from a company report, not text from any standard.
*Synthetic illustration only.* During the year, we completed formal performance-and-development conversations for 1,260 of our 1,400 employees, equivalent to 90% of the workforce. - By gender, 92% of women and 88% of men had a review. - By job level, the figures were 94% for senior leaders, 91% for managers and 89% for other staff.
This example shows how to describe coverage of regular review conversations using a simple narrative, while splitting the result by gender and by employee level. The figures are internally consistent and illustrative only.
*Synthetic illustration only.* We carried out annual development reviews for 735 of our 900 people, which is 82% overall. - By gender, 84% of women and 80% of men were covered. - By employee group, 90% of supervisors, 83% of skilled operatives and 75% of office staff received a review.
This example demonstrates a second way to present the same disclosure, using different sector language and a different employee grouping. The numbers are made up for training purposes and remain mathematically consistent.
How companies report GRI 404-3
Real reports where this topic is disclosed. These are report practice, not exact disclosure templates to copy.

Scenarios to work through
A people team has headcount records for the year and a review tracker showing which staff had a scheduled performance-and-development conversation during the reporting period. Some workers joined late in the year, and a few were on long leave, so the team is unsure whether to count them in the total.
A business has separate employee groups: permanent staff, fixed-term staff, and a small number of interns. The HR system can show review completion for each group, but the team is unsure whether the disclosure should be split by gender and employee category or presented only as one overall figure.
At year-end, the HR team finds that some managers held annual review meetings, while others used mid-year check-ins and informal coaching notes. They want to count all of these as the same thing because each employee had some form of development discussion.
A preparer has calculated that 84 out of 120 employees had a regular review during the year, which gives 70%. The same dataset shows 50 of 60 women and 34 of 60 men received reviews, but the team is unsure whether the subgroup figures need to align with the overall total.
Related framework references
How this disclosure maps across the major reporting frameworks.
Questions this page answers
Start with the plain-language explainer, then work through the step-by-step preparation section, the datapoints to prepare, and the draft-output section. The page is designed to help you turn source data into a draft narrative, a content-index line, and supporting visuals.
The page highlights three datapoints to prepare: gender, employee grouping, and review coverage rate. Use those as the core inputs when you are gathering data for the disclosure.
Use the page’s step-by-step preparation guidance and the employee grouping datapoint to decide which workforce groups are included in your dataset. Keep the scope consistent across the data table, narrative, and evidence pack.
The page is useful for a sustainability/ESG manager, HR or data owner, and assurance reviewer, so ownership should sit with the person who can gather the source data and maintain the evidence pack. The workbook and assurance section are there to support that handover and review.
The page includes an evidence pack with five items and four assurance claims to verify, each with a claim, risk, and evidence angle. Use those to build a file that shows how the figures were prepared and checked.
The page lists common reporting gaps and mistakes, so it is a good check against missing scope, weak evidence, or unclear methodology. Review those before finalising the draft so the narrative, data table, and supporting documents line up.
The Download Centre includes a Prep & Assurance workbook in .xlsx format, which is meant to support preparation and assurance readiness. Use it alongside the page’s step-by-step guidance, evidence pack, and draft-output section.
The page includes synthetic illustrative example disclosures, including a quantitative table, to show how the disclosure can be presented. Treat these as examples only and use them to shape your own draft structure and wording.
The draft-output section gives visualisation ideas, narrative starters, and a GRI content-index line. Use those outputs to convert your prepared data into a first draft that is easy to review and update.
The page notes ESRS S1 (Own Workforce) as the closest correspondence, so the data may be reusable across frameworks. That does not mean the reporting requirements are identical, so check the other framework separately before relying on the same dataset.
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