Social Support as a Stress Buffer or Stress Amplifier and the Moderating Role of Implicit Motives: Protocol for a Randomized Study
Bibliographic record
Abstract
BACKGROUND: Previous research shows that providing social support in socioevaluative stress situations reduces participants' stress responses. This stress-buffer effect, however, does not hold for everybody, and some studies even found a stress-amplifying effect of social support. Motive disposition research suggests that social motives (affiliation and power) lead to differential and sometimes even opposing affective and physiological responses to interpersonal interaction processes. We here integrate both lines of research and hypothesize that participants with strong affiliation motives benefit, while participants with strong power motives do not benefit from social support in terms of psychobiological responses to a given stressor. Further, participants with strong affiliation and power motives are expected to respond to social support with the arousal of motive-specific affects and reproductive hormone responses (affiliation: progesterone; power: estradiol and testosterone). In addition, we test sex differences in the response to social support and in the strengths of social motives. OBJECTIVE: The main objective of this study is to test whether social motives and participants' sex moderate the effects of social support in stressful situations. METHODS: We aim to collect data from 308 participants recruited at our local university. Participants' social motives are assessed using a standardized measure in motive research (Picture Story Exercise). Then, the Trier Social Stress Test for Groups (TSST-G) is used to experimentally induce psychosocial stress. One group of participants receives social support from an associate of the experimenter, while the control group does not receive social support. Stress responses will be assessed by a modified version of the state anxiety scale of the State-Trait Anxiety Inventory and by physiological indicators of stress (cortisol and α-amylase from saliva samples) at 7 measurement points. Reproductive hormones will be analyzed in 4 of these 7 saliva samples. Heart rate and heart rate variability will be assessed continuously. We will additionally measure participants' performance in an interview (part of the TSST-G) using a self-developed categorization system. RESULTS: The Ethics Committee of the University of Constance approved the application to conduct the study on December 18, 2018. Furthermore, the study was retrospectively registered in the German Clinical Trials Register (DKRS; ID: DRKS00028503) on March 09, 2022. The start of the experiment was planned for the beginning of 2019, but was postponed to June 2021 due to COVID-19. Publication of the first results is planned for spring 2023. CONCLUSIONS: Our theory-driven integration of social motives in social support research and the precise analysis of sex differences might disentangle inconsistent findings in TSST research. The more faceted view on individual differences has direct implications for applied contexts as it provides a framework for tailored conceptualizations of social support programs. TRIAL REGISTRATION: German Clinical Trials Register DRKS00028503; https://tinyurl.com/5a87x4da. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/39509.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.060 | 0.011 |
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".