Tarefa causcibench_real.q174

Veredito

Pergunta feita ao analista

What is the effect of WhatsApp messages on individuals's knowledge about Covid19?

Valores esperados e de onde vêm

Verificações

Caminho de referência e caminho do agente

A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

Trajetória

Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

1. 0Início

   What is the effect of WhatsApp messages on individuals's knowledge about Covid19?

   bowles_countering (agent)

   The data aims to test whether trusted WhatsApp messages can counter COVID-19 misinformation in Zimbabwe. Partnering with Internews and Kubatana, the researchers crafted short weekly messages: Week 1 emphasized transmission and social distancing, Week 2 debunked fake cures. At the WhatsApp broadcast list level, timing was randomized: treatment groups received messages on Monday, control groups on Saturday. To measure effects, a survey was sent on Thursday. Knowledge was assessed through factual questions, and behavior through a list experiment, where respondents received either a short list (only non-sensitive activities) or a long list (the same activities plus a sensitive one: visiting others during lockdown). Broadcast lists were blocked in groups of four, with random assignment across timing (treatment vs. control) and list length (short vs. long). Data variables ResponseId: identifier for the responses from the individuals Behavior: number of activities the respondent participated in, coded from 0 to 5 Id: identifier for individuals participating List: binary variable, equals 1 if the WhatsApp broadcast list is assigned to the long experimental list qualtrics: binary variable indicating response mode (1 if Qualtrics, 0 if WhatsApp) T: treatment indicator, equals 1 if the WhatsApp broadcast list is assigned to the treatment group female: binary variable, equals 1 if the respondent is female c_Q4: survey question on factual knowledge about COVID-19 transmission or prevention c_Q5: survey question on factual knowledge about misinformation or fake cures Knowledge: z-score of the respondent's answers to the factual questions c_Q4_1: sub-item of Q4 (first option, coded 1 if correct, 0 otherwise) c_Q4_3: sub-item of Q4 (third option, coded 1 if correct, 0 otherwise) c_Q4_4: sub-item of Q4 (fourth option, coded 1 if correct, 0 otherwise) c_Q4_5: sub-item of Q4 (fifth option, coded 1 if correct, 0 otherwise) block: randomization block (grouping of broadcast lists created at similar times) length_subscribe: number of months the WhatsApp broadcast list had been active Week: indicator for intervention week (1 = distancing message, 2 = misinformation/cures message) response_rate: fraction of weekly responses coming from WhatsApp per person Week_id: numeric identifier for the week urban: binary variable, equals 1 if the individual lived in an urban district

   file 3107fc57c0d67588eccecf4788cf3600ab114f22fa27694bebd9c1892a8e5049 · frame —
2. 1Turno do modelo
   call_f05cc862referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: ols

   Chamadas pedidas

   • infer.ols

     {
       "controls": [
         "qualtrics",
         "female",
         "urban",
         "Week",
         "block"
       ],
       "dataset": "bowles_countering",
       "x": [
         "T"
       ],
       "y": "Knowledge"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.olsv0.1.0call_b1d0bb8fdecidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: ols

   Argumentos

   {
     "cluster": null,
     "controls": [
       "qualtrics",
       "female",
       "urban",
       "Week",
       "block"
     ],
     "dataset": "bowles_countering",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "T"
     ],
     "y": "Knowledge"
   }

   Figuras call_b1d0bb8f

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.T Coefficient on T in the regression of Knowledge	0,258694	0.25869400706938783	0,06735670.06735672729050846	0,126491 – 0,390897

   Diagnósticos

   • n_obs = 864864
   • k = 77
   • r2 = 0,03650780.03650782802884589
   • df_resid = 857857
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 0.3582934851732035,
         "ci_low": -0.2363727651471442,
         "coef": 0.060960360013029645,
         "p": 0.6874844746774621,
         "se": 0.15148911205304655,
         "t": 0.40240753402583357,
         "term": "Intercept"
       },
       {
         "ci_high": 0.390897476707757,
         "ci_low": 0.12649053743101865,
         "coef": 0.25869400706938783,
         "p": 0.00013174794794353436,
         "se": 0.06735672729050846,
         "t": 3.840655825715,
         "term": "T"
       },
       {
         "ci_high": -0.15813405557057456,
         "ci_low": -0.48185296549633483,
         "coef": -0.3199935105334547,
         "p": 0.00011232965806124212,
         "se": 0.08246624084184316,
         "t": -3.8802970435762947,
         "term": "qualtrics"
       },
       {
         "ci_high": 0.06646909367889105,
         "ci_low": -0.2061124815549097,
         "coef": -0.06982169393800931,
         "p": 0.31493586262652384,
         "se": 0.06943918672355207,
         "t": -1.0055085209448098,
         "term": "female"
       },
       {
         "ci_high": 0.08698791508839931,
         "ci_low": -0.2279215739449375,
         "coef": -0.0704668294282691,
         "p": 0.37997449997829463,
         "se": 0.08022207220443374,
         "t": -0.8783970233116779,
         "term": "urban"
       },
       {
         "ci_high": 0.16240188539982967,
         "ci_low": -0.11570778901608872,
         "coef": 0.023347048191870473,
         "p": 0.7418283095906059,
         "se": 0.07084745032685735,
         "t": 0.32953970939191174,
         "term": "Week"
       },
       {
         "ci_high": 0.004196856876401295,
         "ci_low": -0.010469653039181459,
         "coef": -0.0031363980813900816,
         "p": 0.40144938136705777,
         "se": 0.0037362412325097276,
         "t": -0.8394527778612635,
         "term": "block"
       }
     ]
   }

   • 4 rows were dropped because one of 'Knowledge', 'T', 'qualtrics', 'female', 'urban', 'Week', 'block' was missing

   Digests dos dados

   bowles_countering ce0afeaa1f0f9b2b7f6ca76a07d3288fa4145d73a3a3d4de3128c4e148972b63

   sha256 do resultado

   02ec709e11722ab149e2adeea8e9c500ce764a2ae7436a841d27cbead4314dca

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_3847474dreferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.ols): coef.T = 0,2587.",
       "caveats": [],
       "figure_refs": [
         "call_b1d0bb8f:coef.T"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
5. 4Guarda
   resposta aceita

   Números encontrados

   0,2587

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
6. 5Resposta

   Estimativa (infer.ols): coef.T = 0,2587.

   Figuras citadas: call_b1d0bb8f:coef.T · Parada: answered

   Figura da resposta: call_b1d0bb8f:coef.T = 0,2586940.25869400706938783 (se 0,06735670.06735672729050846)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória0.2586940071 vs 0.269

Épocas × verificações

Tentativas

Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

• época 1, tentativa 1passoupontuada(exibindo)

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