Informational and emotional elements in online support groups: a Bayesian approach to large-scale content analysis.
Objective: This research examines the extent to which informational and emotional elements are employed in online support forums for 14 purposively sampled chronic medical conditions and the factors that influence whether posts are of a more informational or emotional nature.Methods: Large-scale qua...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 23; no. 3; pp. 508 - 514 |
|---|---|
| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
May2016
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=116910464&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 116910464 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: May2016 vid: 23 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 116910464 116910464 NLM26911815 116910464 10.1093/jamia/ocv190 NLM26911815 116910464 ppf: 508 ppct: 6 formats: tig: atl: Informational and emotional elements in online support groups: a Bayesian approach to large-scale content analysis. aug: au: Deetjen, Ulrike Powell, John A. affil: Oxford Internet Institute, University of Oxford, Oxford, UK sug: subj: Emotions Consumer Health Information Internet Support Groups Algorithms Support, Psychosocial Male Female Probability Human Male Female ab: Objective: This research examines the extent to which informational and emotional elements are employed in online support forums for 14 purposively sampled chronic medical conditions and the factors that influence whether posts are of a more informational or emotional nature.Methods: Large-scale qualitative data were obtained from Dailystrength.org. Based on a hand-coded training dataset, all posts were classified into informational or emotional using a Bayesian classification algorithm to generalize the findings. Posts that could not be classified with a probability of at least 75% were excluded.Results: The overall tendency toward emotional posts differs by condition: mental health (depression, schizophrenia) and Alzheimer's disease consist of more emotional posts, while informational posts relate more to nonterminal physical conditions (irritable bowel syndrome, diabetes, asthma). There is no gender difference across conditions, although prostate cancer forums are oriented toward informational support, whereas breast cancer forums rather feature emotional support. Across diseases, the best predictors for emotional content are lower age and a higher number of overall posts by the support group member.Discussion: The results are in line with previous empirical research and unify empirical findings from single/2-condition research. Limitations include the analytical restriction to predefined categories (informational, emotional) through the chosen machine-learning approach.Conclusion: Our findings provide an empirical foundation for building theory on informational versus emotional support across conditions, give insights for practitioners to better understand the role of online support groups for different patients, and show the usefulness of machine-learning approaches to analyze large-scale qualitative health data from online settings. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|