MétaCan
Menu
Back to cohort
Record W4406687278 · doi:10.2196/64757

Informal Caregivers Connecting on the Web: Content Analysis of Posts on Discussion Forums

2025· article· en· W4406687278 on OpenAlexvenueno aff
Michelle Foster, Chinenye Egwuonwu, Erin Vernon, Mohammad Alarifi, M. Courtney Hughes

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisPsychologyContent analysisInterpersonal communicationInformal learningSocial psychologyQualitative researchSociology

Abstract

fetched live from OpenAlex

Background: About 53 million adults in the United States offer informal care to family and friends with disease or disability. Such care has an estimated economic value of US $600 million. Most informal caregivers are not paid nor trained in caregiving, with many experiencing higher-than-average levels of stress and depression and lower levels of physical health. Some informal caregivers participate in web-based forums related to their caregiving role. Objective: This study aimed to explore how informal caregivers use easy-to-access caregiving web-based forums, including the types of information they share and seek from others. It also aimed to gain insights into the informal caregiver experience from the content these informal caregivers posted. Methods: The study population consisted of participants who posted on 5 web-based forums for informal caregivers between February and April 2024. Researchers extracted the first 6 responses to the first 20 questions and comments to appear posted by the informal caregivers in each of the 5 forums, removing any individually identifying information. We used a codebook thematic analysis approach to examine the data with Dedoose (SocioCultural Research Consultants). Researchers independently read all posts and coded the data. The author group discussed the codes, reiteratively refined them, and identified themes within the data. Results: The data consisted of 100 initial posts and 600 responses. Over half of the initial posts included specific questions, with the remaining initial posts sharing experiences or reflections. Posts ranged in length from a sentence to more than 500 words. Domains identified included handling interpersonal challenges, navigating complicated systems, gathering tactical coping strategies, managing emotions, and connecting with others in similar situations. Negative interpersonal interactions were mentioned 123 times, with 77 posts describing challenging situations with extended family. Posters inquired about accessing resources, with health care and health insurance included 51 times, while legal and financial concerns were addressed 124 times. Caregiving challenges were mentioned hundreds of times, including discussion of hygiene (n=18), nutrition (n=21), and desire for a caregiving break (n=47). Posters expressed emotion in their comments 180 times, which included 32 mentions of guilt and 26 mentions of positive emotion. The importance of web-based group support was mentioned 301 times. Conclusions: Informal caregivers play an essential role in society. Many experience multifaceted challenges related to their caregiving role, and some turn to the internet for community. Accessing web-based discussion forums is a low-barrier method for informal caregivers to connect with others who may be experiencing similar emotions and challenges. Gaining a greater understanding of the ways informal caregivers seek advice and offer support to one another provides insight into the challenges they face. The domains identified on these forums may be helpful, as clinicians provide information to care recipients and their informal caregivers along their health journeys.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.037
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.006
Science and technology studies0.0020.001
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.233
GPT teacher head0.497
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Formative ResearchSame topicFamily and Patient Care in Intensive Care UnitsFrench-language works237,207