Techniques for Improving Communication of Emotional Content in Text-Only Web-Based Therapeutic Communications: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Web-based typed exchanges are increasingly used by professionals to provide emotional support to patients. Although some empirical evidence exists to suggest that various strategies may be used to convey emotion during Web-based text communication, there has been no critical review of these data in patients with chronic conditions. OBJECTIVES: The objective of this review was to identify the techniques used to convey emotion in written or typed Web-based communication and assess the empirical evidence regarding impact on communication and psychological outcomes. METHODS: An electronic search of databases, including MEDLINE, CINAHL, PsycINFO, EMBASE, and the Cochrane Library was conducted to identify literature published from 1990 to 2016. Searches were also conducted using Google Scholar, manual searching of reference lists of identified papers and manual searching of tables of contents for selected relevant journals. Data extraction and coding were completed by 2 reviewers (10.00% [573/5731] of screened papers, at abstract/title screening stage; 10.0% of screened [69/694] papers, at full-text screening stage). Publications were assessed against the eligibility criteria and excluded if they were duplicates, were not published in English, were published before 1990, referenced animal or nonhuman subjects, did not describe original research, were not journal papers, or did not empirically test the effect of one or more nonverbal communication techniques (for eg, smileys, emoticons, emotional bracketing, voice accentuation, trailers [ellipsis], and pseudowords) as part of Web-based or typed communication on communication-related variables, including message interpretation, social presence, the nature of the interaction (eg, therapeutic alliance), patient perceptions of the interaction (eg, participant satisfaction), or psychological outcomes, including depression, anxiety, and distress. RESULTS: A total of 6902 unique publications were identified. Of these, six publications met the eligibility criteria and were included in a narrative synthesis. All six studies addressed the effect of smileys or emoticons on participant responses, message interpretation, or social presence of the writer. None of these studies specifically targeted chronic conditions. It was found that emoticons were more effective in influencing the emotional impact of a message than no cue and that smileys and emoticons were able to convey a limited amount of emotion. No studies addressed other techniques for conveying emotion in written communication. No studies addressed the effects of any techniques on the nature of the interaction (eg, therapeutic alliance), patient perceptions of the interaction (eg, participant satisfaction), or psychological outcomes (depression, anxiety, or distress). CONCLUSIONS: There is a need for greater empirical attention to the effects of the various proposed techniques for conveying emotion in Web-based typed communications to inform health service providers regarding best-practice communication skills in this setting.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".