To code a health literature review, draft a codebook, pilot it, double-code, reconcile, and record every decision for transparent synthesis.
What Coding Means In A Literature Review
Coding turns text, tables, and figures from source studies into structured entries you can sort, compare, and synthesize. Each tag or field answers a small, repeatable question. Done well, coding reduces noise, guards against bias, and keeps your review reproducible.
You can code quantitative effects, qualitative themes, study features, and risk-of-bias judgements. The secret is a clear rulebook, consistent training, and an audit trail that shows how calls were made.
Coding Workflow At A Glance
| Stage | Main Actions | Typical Outputs |
|---|---|---|
| Plan | Define question, eligibility, outcomes, and units of analysis | Protocol, variables list, screening rules |
| Design | Draft the codebook, pick tools, set naming schemes | Versioned codebook, variable dictionary |
| Pilot | Code a small sample in pairs and compare | Refined rules, resolved ambiguities |
| Train | Run calibration rounds until agreement stabilizes | Inter-rater stats, training notes |
| Code | Double-code all included studies; flag conflicts | Filled extraction forms, conflict log |
| Reconcile | Discuss mismatches and document final calls | Consensus data, decision log |
| Appraise | Judge study quality with fit-for-purpose tools | RoB tables, appraisal notes |
| Synthesize | Roll up fields into summary tables and models | Meta-analysis datasets, thematic maps |
| Report | Describe methods and share forms and logs | Supplementary files, reproducible archive |
Coding A Literature Review For Health Studies: Step-By-Step
This section lays out a practical path you can apply in clinical, public health, or health policy reviews. The same rhythm works for randomized trials, observational studies, mixed-methods papers, and qualitative work.
Set The Review Question
Lock the population, intervention or exposure, comparator, and outcomes. Name the study designs and settings you will include. These choices drive every variable that follows.
Map Study Types And Outcomes
List the designs you expect to see—trials, cohorts, case-control, cross-sectional, process evaluations, ethnographies. For each outcome, state its scale, time point, and unit. Doing this early keeps extraction forms tidy later.
Build The Codebook
Your codebook is the contract between coders. Keep fields short, specific, and testable. For each field, include:
- Name — concise and consistent, e.g.,
followup_weeks. - Type — categorical, numeric, text, or date.
- Allowed Values — picklists or ranges to cut free-text drift.
- Definition — the exact question the field answers.
- Source Location — where to look first in a paper.
- Examples — filled entries from real studies.
Decisions To Predefine
Decide how to treat multi-arm trials, overlapping samples, cluster designs, adjusted vs unadjusted effects, and units like per-patient vs per-visit. For qualitative material, define what counts as a theme versus a subtheme and how to handle participant quotes.
Plan your reporting while you build. The PRISMA 2020 checklist helps shape method descriptions and supplementary files. For extraction and consensus methods, Cochrane Handbook Chapter 5 gives concrete guidance on forms, double coding, and error control.
Pilot And Refine
Pick five to ten varied studies. Two people code them independently. Then compare line by line. Anywhere you debate, rewrite the rule so a third coder would land on the same answer. Repeat with fresh papers until conflicts fall to a trickle.
Train And Calibrate Coders
Run short, timed rounds. Share quick memos that explain tricky edge cases. Track agreement with simple counts or statistics if you like. The goal is steady, consistent calls, not fancy math.
Code In Pairs
Every included study gets two passes by different people. Work in a platform that supports blinding and a clear conflict queue. When a field is unclear in the article, leave a short note that cites the page and figure.
Reconcile And Log Decisions
Meet often. Triage easy conflicts first, then move to judgement-heavy fields. Write one-sentence rules that capture each resolution and paste them into the codebook. Tag the lines that changed, so the team can see the history.
Extract Quantitative Results
For effects, capture the statistic, its measure of variability, the analysis population, the time point, and any adjustment set. When an article reports multiple models, pick the one that matches your protocol and record why.
Capture Qualitative Findings
Use open codes during piloting, then move to a stable set of themes. Link each theme to study identifiers and thick quotes. Keep a short memo for each theme describing how it differs from nearby themes and where it fits in your map.
Assess Study Quality
Match the appraisal tool to the design. Trials often use RoB 2; mixed-methods work may use MMAT; qualitative papers may use JBI’s checklists. Code the judgement per domain, not just a single overall label, and store the support for each call.
Roll Up Codes For Synthesis
Before you pool numbers or write themes, freeze the dataset and version the files. Build derivations openly: how you converted medians, how you aligned scales, how you grouped related themes. Link each table back to the fields it came from.
Practical Tools, Fields, And Tips
The choices below keep forms lean while still rich enough for meta-analysis and thematic synthesis. Tailor labels to your topic, but keep the structure.
Core Study-Level Fields
- Identifiers: DOI, registry number, trial acronym.
- Setting: country, care level, recruitment site.
- Design: trial, cohort, case-control, cross-sectional, qualitative design.
- Sample: N randomized, N analyzed, age group, key inclusion criteria.
- Intervention or exposure: name, dose, duration, delivery.
- Comparator: active, placebo, usual care, none.
- Outcomes: name, unit, time point, measurement tool.
Risk-Of-Bias And Appraisal Fields
- Randomization and allocation concealment (trials).
- Deviations from intended interventions.
- Missing outcome data and handling.
- Measurement of outcome and blinding.
- Selective reporting and protocol availability.
- Relevance and coherence (qualitative).
Outcome-Level Fields
- Effect type: risk ratio, odds ratio, mean difference, hazard ratio.
- Numbers extracted: events and totals, means and SDs, coefficients and SEs.
- Analysis details: adjusted or unadjusted, model name, covariates.
- Time window and follow-up length.
- Scale conversions and imputation flags.
Theme-Level Fields (Qualitative)
- Theme label and short definition.
- Data source: interview, focus group, observation, document.
- Quote anchor: participant ID or page line.
- Frequency count or salience tag if used.
- Link to higher-order theme or domain.
Pick Tools That Fit The Team
Choose software that supports blinded double coding, conflict queues, and clean exports. Spreadsheets suit small projects; bigger teams may prefer web tools with roles and activity logs. You should be able to export to CSV or JSON without loss. Check whether the tool preserves original values and coder notes for each field. Look for templates, bulk edit, and filters that slice by outcome, design, or site. If papers include sensitive material, store files in a secure space with access rules and backups. Keep a copy of the codebook in version control. Before launch, test imports and exports on two dummy studies, then delete them so your database starts clean.
File Hygiene And Versioning
Name files predictably, e.g., 2025-09-18_codebook_v03.xlsx. Store forms, logs, and scripts in a shared space with read-only snapshots at milestones. Put change notes at the top of the codebook and use short tags like [NEW], [CLARIFIED], or [DEPRECATED].
Inter-Rater Agreement Without Drama
Track raw agreement first. If you use statistics, keep them simple and interpret them in context. Discrepancies teach more than a single score. Use them to sharpen wording and point to fields that need better anchors.
Codebook Domains And Examples
Here’s a compact menu you can adapt. It works for intervention reviews, health services studies, and qualitative syntheses alike.
| Domain | Example Codes | Notes |
|---|---|---|
| Population | Adults; adolescents; condition subtype; risk tier | Match eligibility and report key baselines |
| Intervention/Exposure | Drug class; dose bands; session count | Record delivery mode and fidelity markers |
| Comparator | Placebo; usual care; active control | Describe co-interventions if present |
| Outcomes | Primary endpoint; safety; quality of life | State unit, instrument, and timing |
| Effect | RR; OR; MD; HR; thematic label | Store model and adjustment set |
| Context | Clinic; community; telehealth | Add country income tier or region |
| Equity | Age; sex; SES; rurality | Use PROGRESS-Plus style tags |
| Quality | RoB 2 domain calls; MMAT items | Keep quotes or page cites for support |
Write Methods So Others Can Repeat Them
State who coded what, how independence was kept, how conflicts were handled, and where logs can be found. Share blank and filled forms. Link data back to figures and tables so a reader can trace each number or theme to its source.
Make Tables Do The Heavy Lifting
Build summary tables straight from coded fields. Keep labels identical between forms and tables. That way a reader can scan a table header and know exactly which form field produced it.
When Evidence Is Messy
If multiple reports describe the same study, tie them under one study ID and record which report supplied each field. When outcomes differ across time points or instruments, predefine a hierarchy and stick to it. When measures need conversion, keep the formula and the source in a small methods box.
Common Pitfalls And Fast Fixes
- Vague fields. Replace open text with short picklists.
- Skipping piloting. Spend a day piloting; save a week later.
- One-pass coding. Use two coders; you will catch more errors.
- Hidden edits. Version the codebook and keep a visible change log.
- Tool lock-in. Keep exports in open formats you can re-use.
Action Snapshot
Draft a lean codebook today, pilot on five varied papers, and schedule a short reconciliation meeting. Save the rules you write in that session, tag the changes, and roll the updated codebook to the full set. Small, steady moves beat a giant rewrite late in the project.