{"id":"W45936924","doi":"10.7748/nr2010.01.17.2.74.c7464","title":"Using social exchange theory to guide successful study recruitment and retention","year":2010,"lang":"en","type":"article","venue":"Nurse Researcher","topic":"Opinion Dynamics and Social Influence","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Attrition; Social exchange theory; Limiting; Context (archaeology); Psychology; Social psychology; Selection (genetic algorithm); Power (physics); Applied psychology; Computer science; Medicine; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000984348,0.00008727211,0.0001120403,0.00005997939,0.0002824772,0.0001129934,0.0001277343,0.00004285181,0.0006281152],"category_scores_gemma":[0.00002007305,0.00008158865,0.00003950931,0.0001425201,0.00007905091,0.00009645231,0.00008544452,0.0002652765,0.00002104049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003353054,"about_ca_system_score_gemma":0.00005289068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001013275,"about_ca_topic_score_gemma":0.00008894147,"domain_scores_codex":[0.9989746,0.0001716769,0.0001344678,0.0002189824,0.00024511,0.0002551499],"domain_scores_gemma":[0.999545,0.00003770126,0.00003555893,0.0001485015,0.000114573,0.0001186834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001444847,0.001232767,0.2931211,0.00003209424,0.0002349874,0.000009771477,0.03004907,0.000009237106,0.02803935,0.1713031,0.002784851,0.4730392],"study_design_scores_gemma":[0.003816553,0.0007393038,0.7334166,0.00008834062,0.0001140997,0.000001656703,0.06212502,0.002714507,0.001164637,0.08379385,0.1107068,0.001318637],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885648,0.000008681007,0.0006064167,0.0003932911,0.0001626738,0.0007080595,0.00001946221,0.00001274766,0.00952384],"genre_scores_gemma":[0.9965425,9.7917e-7,0.0003789584,0.00002896753,0.0004550392,0.00008940579,0.000006009685,0.0000156686,0.002482461],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4717205,"threshold_uncertainty_score":0.6877421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1560100359120876,"score_gpt":0.4579964099045047,"score_spread":0.3019863739924171,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}